Apparatus and method for motion blur with a dynamic quantization grid

ABSTRACT

Apparatus and method for processing motion blur operations. For example, one embodiment of a graphics processing apparatus comprises: a bounding volume hierarchy (BVH) generator to build a BVH comprising hierarchically-arranged BVH nodes based on input primitives, at least one BVH node comprising one or more child nodes; and motion blur processing hardware logic to determine motion values for a quantization grid based on motion values of the one or more child nodes of the at least one BVH node and to map linear bounds of each of the child nodes to the quantization grid.

BACKGROUND Field of the Invention

This invention relates generally to the field of graphics processors.More particularly, the invention relates to an apparatus and method forimplementing motion blur with a dynamic quantization grid.

Description of the Related Art

Path tracing is an existing technique for rendering photorealisticimages for special effects in films, animated movies, and professionalvisualization. Generating these realistic images requires thecomputation of a physical simulation of light transport in a virtual 3Dscene, using ray tracing as a tool for visibility queries. A highperformance implementation of these visibility queries requiresconstruction of a 3D hierarchy over the scene primitives (typicallytriangles) in a preprocessing phase. The hierarchy allows the raytracing step to quickly determine the closest intersection point betweena ray and a primitive (triangle).

Motion blur is an important feature in photorealistic rendering ofanimations, where the effect of objects moving in the scene while thecamera shutter is open is simulated. Simulating this effect results inan oriented blur of the moving objects, which causes the animation toappear smooth when played back. Rendering motion blur requires randomlysampling the time for each ray path evaluated, and the average over manyof these paths provides the desired blur effect. To implement thistechnique, the underlying ray tracing engine has to be capable oftracing a ray through the scene at an arbitrary time inside the camerashutter interval. This requires an encoding of the motion of thegeometric object inside the spatial acceleration structure used for raytracing.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors;

FIG. 2 is a block diagram of one embodiment of a processor having one ormore processor cores, an integrated memory controller, and an integratedgraphics processor;

FIG. 3 is a block diagram of one embodiment of a graphics processorwhich may be a discreet graphics processing unit, or may be graphicsprocessor integrated with a plurality of processing cores;

FIG. 4 is a block diagram of an embodiment of a graphics-processingengine for a graphics processor;

FIG. 5 is a block diagram of another embodiment of a graphics processor;

FIGS. 6A-B illustrate examples of execution circuitry and logic;

FIG. 7 illustrates a graphics processor execution unit instructionformat according to an embodiment;

FIG. 8 is a block diagram of another embodiment of a graphics processorwhich includes a graphics pipeline, a media pipeline, a display engine,thread execution logic, and a render output pipeline;

FIG. 9A is a block diagram illustrating a graphics processor commandformat according to an embodiment;

FIG. 9B is a block diagram illustrating a graphics processor commandsequence according to an embodiment;

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system according to an embodiment;

FIGS. 11A-B illustrate an exemplary IP core development system that maybe used to manufacture an integrated circuit and an exemplary packageassembly;

FIG. 12 illustrates an exemplary system on a chip integrated circuitthat may be fabricated using one or more IP cores, according to anembodiment;

FIGS. 13A-B illustrates an exemplary graphics processor of a system on achip integrated circuit that may be fabricated using one or more IPcores;

FIG. 14A-B illustrate exemplary graphics processor architectures;

FIG. 15 is an illustration of a bounding volume, according toembodiments;

FIGS. 16A-B illustrate a representation of a bounding volume hierarchy;

FIG. 17 is an illustration of a ray-box intersection test, according toan embodiment;

FIG. 18 is a block diagram illustrating an exemplary quantized BVH nodeaccording to an embodiment;

FIG. 19 is a block diagram of a composite floating point data block foruse by a quantized BVH node according to a further embodiment;

FIG. 20 illustrates ray-box intersection using quantized values todefine a child bounding box relative to a parent bounding box, accordingto an embodiment;

FIG. 21 is a flow diagram of BVH decompression and traversal logic,according to an embodiment;

FIG. 22 is an illustration of an exemplary two-dimensional shared planebounding box;

FIG. 23 is a flow diagram of shared plane BVH logic, according to anembodiment;

FIG. 24 is a block diagram of a computing device including a graphicsprocessor having bounding volume hierarchy logic, according to anembodiment;

FIG. 25 illustrates an apparatus or system on which embodiments of theinvention may be implemented;

FIG. 26 illustrates one embodiment of an apparatus for building,compressing and decompressing nodes of a bounding volume hierarchy;

FIG. 27 one embodiment in which leaf nodes are compressed by replacingpointers with offsets;

FIG. 28 illustrates code associated with three BVH node types;

FIG. 29 compares embodiments of the invention with existingimplementations with respect to memory consumption (in MB) and totalrendering performance (in fps);

FIG. 30 is used to compare existing implementations with embodiments ofthe invention with respect to memory consumption (in MB), traversalstatistics and total performance;

FIG. 31 illustrates a naïve extension of quantized bounding boxes tomotion blurred triangles;

FIG. 32 illustrates one embodiment of the invention which uses smallerquantization grids at the start and end times;

FIG. 33 illustrates one embodiment of an architecture including motionblur processing hardware/logic; and

FIG. 34 illustrates a method in accordance with one embodiment of theinvention.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention described below. Itwill be apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without some of thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form to avoid obscuring the underlyingprinciples of the embodiments of the invention.

Exemplary Graphics Processor Architectures and Data Types SystemOverview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. In various embodiments the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In one embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

In one embodiment the system 100 can include, or be incorporated withina server-based gaming platform, a game console, including a game andmedia console, a mobile gaming console, a handheld game console, or anonline game console. In some embodiments the system 100 is a mobilephone, smart phone, tablet computing device or mobile Internet device.The processing system 100 can also include, couple with, or beintegrated within a wearable device, such as a smart watch wearabledevice, smart eyewear device, augmented reality device, or virtualreality device. In some embodiments, the processing system 100 is atelevision or set top box device having one or more processors 102 and agraphical interface generated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI Express), memory busses, or other types of interface busses. Inone embodiment the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random access memory (DRAM)device, a static random access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments adisplay device 111 can connect to the processor(s) 102. The displaydevice 111 can be one or more of an internal display device, as in amobile electronic device or a laptop device or an external displaydevice attached via a display interface (e.g., DisplayPort, etc.). Inone embodiment the display device 111 can be a head mounted display(HMD) such as a stereoscopic display device for use in virtual reality(VR) applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., hard disk drive, flash memory, etc.). The data storage device 124can connect via a storage interface (e.g., SATA) or via a peripheralbus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver.The firmware interface 128 enables communication with system firmware,and can be, for example, a unified extensible firmware interface (UEFI).The network controller 134 can enable a network connection to a wirednetwork. In some embodiments, a high-performance network controller (notshown) couples with the interface bus 110. The audio controller 146, inone embodiment, is a multi-channel high definition audio controller. Inone embodiment the system 100 includes an optional legacy I/O controller140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to thesystem. The platform controller hub 130 can also connect to one or moreUniversal Serial Bus (USB) controllers 142 connect input devices, suchas keyboard and mouse 143 combinations, a camera 144, or other USB inputdevices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112. In one embodiment the platform controller hub 130 and/ormemory controller 1160 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

FIG. 2 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 2having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 3 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 320 can be an internal orexternal display device. In one embodiment the display device 320 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, as well as the Society of Motion Picture &Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic ExpertsGroup (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316is optional in some embodiments of the GPE 410 and may not be explicitlyincluded within the GPE 410. For example and in at least one embodiment,a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 415B), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations. The general-purpose logic can perform processingoperations in parallel or in conjunction with general-purpose logicwithin the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG.2.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented where the demand for a givenspecialized function is insufficient for inclusion within the graphicscore array 414. Instead a single instantiation of that specializedfunction is implemented as a stand-alone entity in the shared functionlogic 420 and shared among the execution resources within the graphicscore array 414. The precise set of functions that are shared between thegraphics core array 414 and included within the graphics core array 414varies across embodiments. In some embodiments, specific sharedfunctions within the shared function logic 420 that are used extensivelyby the graphics core array 414 may be included within shared functionlogic 416 within the graphics core array 414. In various embodiments,the shared function logic 416 within the graphics core array 414 caninclude some or all logic within the shared function logic 420. In oneembodiment, all logic elements within the shared function logic 420 maybe duplicated within the shared function logic 416 of the graphics corearray 414. In one embodiment the shared function logic 420 is excludedin favor of the shared function logic 416 within the graphics core array414.

FIG. 5 is a block diagram of hardware logic of a graphics processor core500, according to some embodiments described herein. Elements of FIG. 5having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Theillustrated graphics processor core 500, in some embodiments, isincluded within the graphics core array 414 of FIG. 4. The graphicsprocessor core 500, sometimes referred to as a core slice, can be one ormultiple graphics cores within a modular graphics processor. Thegraphics processor core 500 is exemplary of one graphics core slice, anda graphics processor as described herein may include multiple graphicscore slices based on target power and performance envelopes. Eachgraphics processor core 500 can include a fixed function block 530coupled with multiple sub-cores 501A-501F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments the fixed function block 530 includes ageometry/fixed function pipeline 536 that can be shared by all sub-coresin the graphics processor core 500, for example, in lower performanceand/or lower power graphics processor implementations. In variousembodiments, the geometry/fixed function pipeline 536 includes a 3Dfixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4)a video front-end unit, a thread spawner and thread dispatcher, and aunified return buffer manager, which manages unified return buffers,such as the unified return buffer 418 of FIG. 4.

In one embodiment the fixed function block 530 also includes a graphicsSoC interface 537, a graphics microcontroller 538, and a media pipeline539. The graphics SoC interface 537 provides an interface between thegraphics processor core 500 and other processor cores within a system ona chip integrated circuit. The graphics microcontroller 538 is aprogrammable sub-processor that is configurable to manage variousfunctions of the graphics processor core 500, including thread dispatch,scheduling, and pre-emption. The media pipeline 539 (e.g., mediapipeline 316 of FIG. 3 and FIG. 4) includes logic to facilitate thedecoding, encoding, pre-processing, and/or post-processing of multimediadata, including image and video data. The media pipeline 539 implementmedia operations via requests to compute or sampling logic within thesub-cores 501-501F.

In one embodiment the SoC interface 537 enables the graphics processorcore 500 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, the systemRAM, and/or embedded on-chip or on-package DRAM. The SoC interface 537can also enable communication with fixed function devices within theSoC, such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicsprocessor core 500 and CPUs within the SoC. The SoC interface 537 canalso implement power management controls for the graphics processor core500 and enable an interface between a clock domain of the graphic core500 and other clock domains within the SoC. In one embodiment the SoCinterface 537 enables receipt of command buffers from a command streamerand global thread dispatcher that are configured to provide commands andinstructions to each of one or more graphics cores within a graphicsprocessor. The commands and instructions can be dispatched to the mediapipeline 539, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline536, geometry and fixed function pipeline 514) when graphics processingoperations are to be performed.

The graphics microcontroller 538 can be configured to perform variousscheduling and management tasks for the graphics processor core 500. Inone embodiment the graphics microcontroller 538 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 502A-502F, 504A-504F withinthe sub-cores 501A-501F. In this scheduling model, host softwareexecuting on a CPU core of an SoC including the graphics processor core500 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on the appropriate graphics engine.Scheduling operations include determining which workload to run next,submitting a workload to a command streamer, pre-empting existingworkloads running on an engine, monitoring progress of a workload, andnotifying host software when a workload is complete. In one embodimentthe graphics microcontroller 538 can also facilitate low-power or idlestates for the graphics processor core 500, providing the graphicsprocessor core 500 with the ability to save and restore registers withinthe graphics processor core 500 across low-power state transitionsindependently from the operating system and/or graphics driver softwareon the system.

The graphics processor core 500 may have greater than or fewer than theillustrated sub-cores 501A-501F, up to N modular sub-cores. For each setof N sub-cores, the graphics processor core 500 can also include sharedfunction logic 510, shared and/or cache memory 512, a geometry/fixedfunction pipeline 514, as well as additional fixed function logic 516 toaccelerate various graphics and compute processing operations. Theshared function logic 510 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics processor core 500. The shared and/or cache memory512 can be a last-level cache for the set of N sub-cores 501A-501Fwithin the graphics processor core 500, and can also serve as sharedmemory that is accessible by multiple sub-cores. The geometry/fixedfunction pipeline 514 can be included instead of the geometry/fixedfunction pipeline 536 within the fixed function block 530 and caninclude the same or similar logic units.

In one embodiment the graphics processor core 500 includes additionalfixed function logic 516 that can include various fixed functionacceleration logic for use by the graphics processor core 500. In oneembodiment the additional fixed function logic 516 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 516, 536, and acull pipeline, which is an additional geometry pipeline which may beincluded within the additional fixed function logic 516. In oneembodiment the cull pipeline is a trimmed down version of the fullgeometry pipeline. The full pipeline and the cull pipeline can executedifferent instances of the same application, each instance having aseparate context. Position only shading can hide long cull runs ofdiscarded triangles, enabling shading to be completed earlier in someinstances. For example and in one embodiment the cull pipeline logicwithin the additional fixed function logic 516 can execute positionshaders in parallel with the main application and generally generatescritical results faster than the full pipeline, as the cull pipelinefetches and shades only the position attribute of the vertices, withoutperforming rasterization and rendering of the pixels to the framebuffer. The cull pipeline can use the generated critical results tocompute visibility information for all the triangles without regard towhether those triangles are culled. The full pipeline (which in thisinstance may be referred to as a replay pipeline) can consume thevisibility information to skip the culled triangles to shade only thevisible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 516 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 501A-501F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 501A-501F include multiple EUarrays 502A-502F, 504A-504F, thread dispatch and inter-threadcommunication (TD/IC) logic 503A-503F, a 3D (e.g., texture) sampler505A-505F, a media sampler 506A-506F, a shader processor 507A-507F, andshared local memory (SLM) 508A-508F. The EU arrays 502A-502F, 504A-504Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 503A-503F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 505A-505F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler506A-506F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 501A-501F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 501A-501F can make use of shared local memory 508A-508F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

Execution Units

FIGS. 6A-6B illustrate thread execution logic 600 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 6A-6B having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 6A illustrates anoverview of thread execution logic 600, which can include a variant ofthe hardware logic illustrated with each sub-core 501A-501F of FIG. 5.FIG. 6B illustrates exemplary internal details of an execution unit.

As illustrated in FIG. 6A, in some embodiments thread execution logic600 includes a shader processor 602, a thread dispatcher 604,instruction cache 606, a scalable execution unit array including aplurality of execution units 608A-608N, a sampler 610, a data cache 612,and a data port 614. In one embodiment the scalable execution unit arraycan dynamically scale by enabling or disabling one or more executionunits (e.g., any of execution unit 608A, 608B, 608C, 608D, through608N-1 and 608N) based on the computational requirements of a workload.In one embodiment the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 600 includes one or more connectionsto memory, such as system memory or cache memory, through one or more ofinstruction cache 606, data port 614, sampler 610, and execution units608A-608N. In some embodiments, each execution unit (e.g. 608A) is astand-alone programmable general-purpose computational unit that iscapable of executing multiple simultaneous hardware threads whileprocessing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 608A-608N is scalableto include any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used toexecute shader programs. A shader processor 602 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 608A-608N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 604 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 608A-608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 608A-608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units608A-608N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 609A-609N having thread control logic (607A-607N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 609A-609N includes at leasttwo execution units. For example, fused execution unit 609A includes afirst EU 608A, second EU 608B, and thread control logic 607A that iscommon to the first EU 608A and the second EU 608B. The thread controllogic 607A controls threads executed on the fused graphics executionunit 609A, allowing each EU within the fused execution units 609A-609Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, a sampler 610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 602dispatches threads to an execution unit (e.g., 608A) via threaddispatcher 604. In some embodiments, shader processor 602 uses texturesampling logic in the sampler 610 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 614 includes or couples to one ormore cache memories (e.g., data cache 612) to cache data for memoryaccess via the data port.

As illustrated in FIG. 6B, a graphics execution unit 608 can include aninstruction fetch unit 637, a general register file array (GRF) 624, anarchitectural register file array (ARF) 626, a thread arbiter 622, asend unit 630, a branch unit 632, a set of SIMD floating point units(FPUs) 634, and in one embodiment a set of dedicated integer SIMD ALUs635. The GRF 624 and ARF 626 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 608.In one embodiment, per thread architectural state is maintained in theARF 626, while data used during thread execution is stored in the GRF624. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 626.

In one embodiment the graphics execution unit 608 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 608 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 622 of the graphics execution unit thread 608 can dispatch theinstructions to one of the send unit 630, branch unit 6342, or SIMDFPU(s) 634 for execution. Each execution thread can access 128general-purpose registers within the GRF 624, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 624, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment up to seven threads can executesimultaneously, although the number of threads per execution unit canalso vary according to embodiments. In an embodiment in which seventhreads may access 4 Kbytes, the GRF 624 can store a total of 28 Kbytes.Flexible addressing modes can permit registers to be addressed togetherto build effectively wider registers or to represent strided rectangularblock data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 630. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 632 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 608 includes one or moreSIMD floating point units (FPU(s)) 634 to perform floating-pointoperations. In one embodiment, the FPU(s) 634 also support integercomputation. In one embodiment the FPU(s) 634 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 64-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 635 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 608 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can chose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 608 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 608 is executed on a different channel.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for thecommand. A sub-opcode 905 and a command size 908 are also included insome commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The package substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1170 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I2S/I2C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13A illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13B illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13A is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12, such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, caches 1325A-1325B, and circuit interconnects1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

FIGS. 14A-14B illustrate additional exemplary graphics processor logicaccording to embodiments described herein. FIG. 14A illustrates agraphics core 1400 that may be included within the graphics processor1210 of FIG. 12, and may be a unified shader core 1355A-1355N as in FIG.13B. FIG. 14B illustrates an additional highly-parallel general-purposegraphics processing unit 1430, which is a highly-parallelgeneral-purpose graphics processing suitableunit suitable for deploymenton a multi-chip module.

As shown in FIG. 14A, the graphics core 1400 includes a sharedinstruction cache 1402, a texture unit 1418, and a cache/shared memory1420 that are common to the execution resources within the graphics core1400. The graphics core 1400 can include multiple slices 1401A-1401N orpartition for each core, and a graphics processor can include multipleinstances of the graphics core 1400. The slices 1401A-1401N can includesupport logic including a local instruction cache 1404A-1404N, a threadscheduler 1406A-1406N, a thread dispatcher 1408A-1408N, and a set ofregisters 1410A-1440N. To perform logic operations, the slices1401A-1401N can include a set of additional function units (AFUs1412A-1412N), floating-point units (FPU 1414A-1414N), integer arithmeticlogic units (ALUs 1416-1416N), address computational units (ACU1413A-1413N), double-precision floating-point units (DPFPU 1415A-1415N),and matrix processing units (MPU 1417A-1417N).

Some of the computational units operate at a specific precision. Forexample, the FPUs 1414A-1414N can perform single-precision (32-bit) andhalf-precision (16-bit) floating point operations, while the DPFPUs1415A-1415N perform double precision (64-bit) floating point operations.The ALUs 1416A-1416N can perform variable precision integer operationsat 8-bit, 16-bit, and 32-bit precision, and can be configured for mixedprecision operations. The MPUs 1417A-1417N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. The MPUs 1417-1417N can perform avariety of matrix operations to accelerate machine learning applicationframeworks, including enabling support for accelerated general matrix tomatrix multiplication (GEMM). The AFUs 1412A-1412N can performadditional logic operations not supported by the floating-point orinteger units, including trigonometric operations (e.g., Sine, Cosine,etc.).

As shown in FIG. 14B, a general-purpose processing unit (GPGPU) 1430 canbe configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units. Additionally, theGPGPU 1430 can be linked directly to other instances of the GPGPU tocreate a multi-GPU cluster to improve training speed for particularlydeep neural networks. The GPGPU 1430 includes a host interface 1432 toenable a connection with a host processor. In one embodiment the hostinterface 1432 is a PCI Express interface. However, the host interfacecan also be a vendor specific communications interface or communicationsfabric. The GPGPU 1430 receives commands from the host processor anduses a global scheduler 1434 to distribute execution threads associatedwith those commands to a set of compute clusters 1436A-1436H. Thecompute clusters 1436A-1436H share a cache memory 1438. The cache memory1438 can serve as a higher-level cache for cache memories within thecompute clusters 1436A-1436H.

The GPGPU 1430 includes memory 14434A-14434B coupled with the computeclusters 1436A-1436H via a set of memory controllers 1442A-1442B. Invarious embodiments, the memory 1434A-1434B can include various types ofmemory devices including dynamic random access memory (DRAM) or graphicsrandom access memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory.

In one embodiment the compute clusters 1436A-1436H each include a set ofgraphics cores, such as the graphics core 1400 of FIG. 14A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example and in oneembodiment at least a subset of the floating point units in each of thecompute clusters 1436A-1436H can be configured to perform 16-bit or32-bit floating point operations, while a different subset of thefloating point units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 1430 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 1430 communicate over thehost interface 1432. In one embodiment the GPGPU 1430 includes an I/Ohub 1439 that couples the GPGPU 1430 with a GPU link 1440 that enables adirect connection to other instances of the GPGPU. In one embodiment theGPU link 1440 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 1430. In one embodiment the GPU link 1440 couples with a highspeed interconnect to transmit and receive data to other GPGPUs orparallel processors. In one embodiment the multiple instances of theGPGPU 1430 are located in separate data processing systems andcommunicate via a network device that is accessible via the hostinterface 1432. In one embodiment the GPU link 1440 can be configured toenable a connection to a host processor in addition to or as analternative to the host interface 1432.

While the illustrated configuration of the GPGPU 1430 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 1430 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 1430 includes fewer of the computeclusters 1436A-1436H relative to the training configuration.Additionally, the memory technology associated with the memory1434A-1434B may differ between inferencing and training configurations,with higher bandwidth memory technologies devoted to trainingconfigurations. In one embodiment the inferencing configuration of theGPGPU 1430 can support inferencing specific instructions. For example,an inferencing configuration can provide support for one or more 8-bitinteger dot product instructions, which are commonly used duringinferencing operations for deployed neural networks.

Apparatus and Method for Bounding Volume Hierarchy (BVH) Compression

An N-wide bounding volume hierarchy (BVH) node includes N boundingvolumes that correspond to the N children of the given node. In additionto a bounding volume, a reference to each child node is included aseither an index or a pointer. One bit of the index or pointer can beassigned to indicate whether the node is an internal node or a leafnode. A commonly used bounding volume format, particularly for raytracing, is the axis-aligned bounding volume (AABV) or axis-alignedbounding box (AABB). An AABB can be defined only with the minimum andmaximum extents in each dimension, providing for an efficient rayintersection test.

Typically, an AABB is stored in an uncompressed format usingsingle-precision (e.g., 4-byte) floating-point value. To define anuncompressed three-dimensional AABB, two single precision floating pointvalues (min/max) for each of three axes are used (e.g., 2×3×4),resulting in 24-bytes to store the extents of the AAAB, plus the indexor pointer to the child node (e.g., a 4-byte integer or an 8-bytepointer). Accordingly, each AABB defined for a BVH node may be up to32-bytes. Thus, a binary BVH node with children may require 64 bytes, a4-wide BVH node may require 128 bytes, and an 8-wide BVH may require upto 256 bytes.

Oriented bounding boxes using discrete oriented polytopes ink-directions (k-DOPs) are also a commonly used bounding volume formatthat may be used with embodiments described herein. For k-DOPs, lowerand upper bounds are stored for multiple arbitrary directions. Incontrast to AABBs, k-DOPs are not limited to bounds in the direction ofthe coordinate axes only, but bound the geometry in any number ofdirections in space.

To reduce the memory size requirements for using a bounding volumehierarchy (BVH), the BVH data may be stored in a compressed format. Forexample, each AABB can be stored in a hierarchically compressed formatrelative the parent of the AABB. However, hierarchical encoding maycause issues with ray tracing implementations when BVH node referencesare pushed on to the stack during ray traversal. When laterdereferenced, the path to the root node is followed to compute the finalAABB, potentially resulting in long dependency chain. An alternativesolution stores the current AABB on the stack, which requires asignificant amount of stack memory to store the additional data, as thestack depth per ray typically ranges between 40 to 60 entries.

Embodiments described herein provide for an apparatus, system, method,and various logical processes for compressing BVH nodes in a simple andefficient manner, without requiring a reference to the parent node orextra stack storage space to decompress the child bounds of a node,significantly reducing the complexity of implementing ray tracingacceleration hardware.

In one embodiment, to reduce memory requirements, N child bounding boxesof an N-wide BVH node are encoded relative to the merged box of allchildren by storing the parent bounding box with absolute coordinatesand full (e.g., floating point) precision, while the child boundingboxes are stored relative to the parent bounding box with lowerprecision.

The approach described herein reduces memory storage and bandwidthrequirements compared to traditional approaches that store fullprecision bounding boxes for all children. Each node may be decompressedseparately of other nodes. Consequently, complete bounding boxes are notstored on the stack during traversal and the entire path from the rootof the tree is not re-traversed to decompress nodes on pop operations.Additionally, ray-node intersection testing can be performed at reducedprecision, reducing the complexity required within the arithmetichardware units.

Bounding Volumes and Ray-Box Intersection Testing

FIG. 15 is an illustration of a bounding volume 1502, according toembodiments. The bounding volume 1502 illustrated is axis aligned to athree dimensional axis 1500. However, embodiments are applicable todifferent bounding representations (e.g., oriented bounding boxes,discrete oriented polytopes, spheres, etc.) and to an arbitrary numberof dimensions. The bounding volume 1502 defines a minimum and maximumextent of a three dimensional object 1504 along each dimension of theaxis. To generate a BVH for a scene, a bounding box is constructed foreach object in the set of objects in the scene. A set of parent boundingboxes can then be constructed around groupings of the bounding boxesconstructed for each object.

FIGS. 16A-B illustrate a representation of a bounding volume hierarchyfor two dimensional objects. FIG. 16A shows a set of bounding volumes1600 around a set of geometric objects. FIG. 16B shows an ordered tree1602 of the bounding volumes 1600 of FIG. 16A.

As shown in FIG. 16A, the set of bounding volumes 1600 includes a rootbounding volume N₁, which is a parent bounding volume for all otherbounding volumes N₂-N₇. Bounding volumes N₂ and N₃ are internal boundingvolumes between the root volume N₁ and the leaf volumes N₄-N₇. The leafvolumes N₄-N₇ include geometric objects O₁-O₈ for a scene.

FIG. 16B shows an ordered tree 1602 of the bounding volumes N₁-N₇ andgeometric objects O₁-O₈. The illustrated ordered tree 1602 is a binarytree in which each node of the tree has two child nodes. A datastructure configured to contain information for each node can includebounding information for the bounding volume (e.g., bounding box) of thenode, as well as at least a reference to the node of each child of thenode.

The ordered tree 1602 representation of the bounding volumes defines ahierarchy that can be used to perform a hierarchical version of variousoperations including, but not limited to collision detection and ray-boxintersection. In the instance of ray-box intersection, nodes can betested in a hierarchical fashion beginning with the root node N₁ whichis the parent node to all other bounding volume nodes in the hierarchy.If the ray-box intersection test for the root node N₁ fails, all othernodes of the tree may be bypassed. If the ray-box intersection test forthe root node N₁ passes, sub-trees of the tree can be tested andtraversed or bypassed in an ordered fashion until, at the least, the setof intersected leaf nodes N₄-N₇ are determined. The precise testing andtraversal algorithms used can vary according to embodiments.

FIG. 17 is an illustration of a ray-box intersection test, according toan embodiment. During the ray-box intersection test, a ray 1702 is castand the equation defining the ray can be used to determine whether theray intersects the planes that define the bounding box 1700 under test.The ray 1702 can be expressed as O+D·t where O corresponds to the originof the ray D is the direction of the ray and t is a real value. Changingt can be used to define any point along the ray. The ray 1702 is said tointersect the bounding box 1700 when the largest entry planeintersection distance is smaller than or equal to the smallest exitplane distance. For the ray 1702 of FIG. 17, the y plane entryintersection distance is shown as t_(min-y) 1704. The y plane exitintersection distance is shown as t_(max-y) 1708. The x plane entryintersection distance can be calculated at t_(min-x) 1706, the x planeexit intersection distance is shown as t_(max-x) 1710. Accordingly, thegiven ray 1702 can be mathematically shown to intersect the boundingbox, at least along the x and y planes, because t_(min-x) 1706 is lessthan t_(max-y) 1708. To perform the ray-box intersection test using agraphics processor, the graphics processor is configured to store anacceleration data structure that defines, at the least, each boundingbox to be tested. For acceleration using a bounding volume hierarchy, atthe least, a reference to the child nodes to the bounding box is stored.

Bounding Volume Node Compression

For an axis-aligned bounding box in 3D space, the acceleration datastructure can store the lower and upper bounds of the bounding box inthree dimensions. A software implementation can use 32-bit floatingpoint numbers to store these bounds, which adds up to 2×3×4=24-bytes perbounding box. For an N-wide BVH node one has to store N boxes and Nchild references. In total, the storage for a 4-wide BVH node is N*24bytes plus N*4 bytes for the child reference, assuming 4 bytes perreference, which results in a total of (24+4)*N bytes, for a total of112 bytes for a 4-wide BVH node and 224 bytes for an 8-wide BVH node.

In one embodiment the size of a BVH node is reduced by storing a singlehigher accuracy parent bounding box that encloses all child boundingboxes, and storing each child bounding box with lower accuracy relativeto that parent box. Depending on the usage scenario different numberrepresentations may be used to store the high accuracy parent boundingbox and the lower accuracy relative child bounds.

FIG. 18 is a block diagram illustrating an exemplary quantized BVH node1810, according to an embodiment. The quantized BVH node 1810 caninclude higher precision values to define a parent bounding box for aBVH node. For example, parent_lower_x 1812, parent_lower_y 1814,parent_lower_z 1816, parent upper_x 1822, parent_upper_y 1824, andparent_upper_z 1826 can be stored using single or double precisionfloating-point values. The values for the child bounding box for eachchild bounding box stored in the node can be quantized and stored aslower precision values, such as fixed point representations for boundingbox values that are defined relative to the parent bounding box. Forexample, child_lower_x 1832, child_lower_y 1834, child_lower_z 1836, aswell as child_upper_x 1842, child_upper_y 1844, and child_upper_z 1846can be stored as lower precision fixed point values. Additionally achild reference 1852 can be stored for each child. The child reference1852 can be an index into a table that stores the location of each childnode or can be a pointer to the child node.

As shown in FIG. 18, a single or double precision floating-point valuemay be used to store the parent bounding box, while M-bit fixed pointvalues may be used to encode the relative child bounding boxes. A datastructure for the quantized BVH node 1810 of FIG. 18 can be defined bythe quantized N-wide BVH node shown in Table 1 below.

TABLE 1 Quantized N-wide BVH Node. struct QuantizedNode { Realparent_lower_x, parent_lower_y, parent_lower_z; Real parent_upper_x,parent_upper_y, parent_upper_z; UintM child_lower_x[N],child_lower_y[N], child_lower_z[N]; 

UintM child_upper_x[N], child_upper_y[N], child_upper_z[N]; Referencechild [N]; };

The quantized node of Table 1 realizes a reduced data structure size byquantizing the child values while maintaining a baseline level ofaccuracy by storing higher precision values for the extents of theparent bounding box. In Table 1, Real denotes a higher accuracy numberrepresentation (e.g. 32-bit or 64-bit floating values), and UintMdenotes lower accuracy unsigned integer numbers using M-bits of accuracyused to represent fixed point numbers. Reference denotes the type usedto represent references to child nodes (e.g. 4-byte indices of 8-bytepointers).

A typical instantiation of this approach can use 32-bit childreferences, single precision floating point values for the parentbounds, and M=8 bits (1 byte) for the relative child bounds. Thiscompressed node would then require 6*4+6*N+4*N bytes. For a 4-wide BVHthis totals 64 bytes (compared to 112 bytes for the uncompressedversion) and for an 8-wide BVH this totals 104 Bytes (compared to 224bytes for the uncompressed version).

To traverse such a compressed BVH node, graphics processing logic candecompress the relative child bounding boxes and then intersect thedecompressed node using standard approaches. The uncompressed lowerbound can then be obtained for each dimension x, y, and z. Equation 1below shows a formula to obtain a child lower_x value.

$\begin{matrix}{{Equation}\mspace{14mu} 1\text{:}\mspace{14mu}{Child}\mspace{14mu}{node}\mspace{14mu}{decompression}\mspace{14mu}{for}\mspace{14mu}{BVH}\mspace{14mu}{{Node}.}} & \; \\{{child}_{lower_{x}} = {{parent}_{lower_{x}} + {{child}_{lower_{x}} \times \frac{{parent}_{upper_{x}} - {parent}_{{lower}_{x}}}{\left( {2^{M} - 1} \right)}}}} & \;\end{matrix}$

In Equation 1 above, M represents the number of bits of accuracy for thefixed point representation of the child bounds. Logic to decompresschild data for each dimension of the BVH node can be implemented as inTable 2 below.

TABLE 2 Child Node Decompression for a BVH Node float child_lower_x =node.parent_lower.x + node.child_lower_x[i]/ (2{circumflex over( )}M−1)*(node.parent_upper_x-node.parent_lower_x); float child_lower_y= node.parent_lower.y + node.child_lower_y[i]/ (2{circumflex over( )}M−1)*(node.parent_upper_y-node.parent_lower_y); float child_lower_z= node.parent_lower.z + node.child_lower_z[i]/ (2{circumflex over( )}M−1)*(node.parent_upper_z-node.parent_lower_z);

Table 2 illustrates a calculation of a floating point value for thelower bounds of a child bounding box based on floating point value forthe extents of the parent pounding box and a fixed point value of achild bounding box that is stored as an offset from an extent of theparent bounding box. The child upper bounds may be computed in ananalogous manner.

In one embodiment the performance of the decompression can be improvedby storing the scaled parent bounding box sizes, e.g.,(parent_upper_x-parent_lower_x)/(2{circumflex over ( )}M−1) instead ofthe parent_upper_x/y/z values. In such embodiment, a child bounding boxextent can be computed according to the example logic shown in Table 3.

TABLE 3 Enhanced Child Node Decompression for a BVH Node floatchild_lower_x = node.parent_lower.x +node.child_lower_x[i]*node.scaled_parent_size_x; float child_lower_y =node.parent_lower.y + node.child_lower_y[i]*node.scaled_parent_size_y;float child_lower_z = node.parent_lower.z +node.child_lower_z[i]*node.scaled_parent_size_z;

Note that in the optimized version the decompression/dequantization canbe formulated as a MAD-instruction (multiply-and-add) where hardwaresupport exists for such instruction. In one embodiment, the operationsfor each child node can be performed using SIMD/vector logic, enablingthe simultaneous evaluation of each child within the node.

While the approach described above approach works well for a shader orCPU based implementation, one embodiment provides specialized hardwarethat is configured to perform ray-tracing operations including ray-boxintersection tests using a bounding volume hierarchy. In such embodimentthe specialized hardware can be configured to store a further quantizedrepresentation of the BVH node data and de-quantize such dataautomatically when performing a ray-box intersection test.

FIG. 19 is a block diagram of a composite floating point data block 1900for use by a quantized BVH node 1910 according to a further embodiment.In one embodiment, in contrast with a 32-bit single precision floatingpoint representation or a 64-bit double precision floating pointrepresentation of the extents of the parent bounding box, logic tosupport a composite floating point data block 1900 can be defined byspecialized logic within a graphics processor. The composite floatingpoint (CFP) data block 1900 can include a 1-bit sign bit 1902, avariable sized (E-bit) signed integer exponent 1904 and a variable sized(K-bit) mantissa 1906. Multiple values for E and K may be configurableby adjusting values stored in configuration registers of the graphicsprocessor. In one embodiment, the values for E and K may beindependently configured within a range of values. In one embodiment afixed set of interrelated values for E and K may be selected from viathe configuration registers. In one embodiment, a single value each forE and K is hard coded into BVH logic of the graphics processor. Thevalues E and K enable the CFP data block 1900 to be used as a customized(e.g., special purpose) floating point data type that can be tailored tothe data set.

Using the CFP data block 1900, the graphics processor can be configuredto store bounding box data in the quantized BVH node 1910. In oneembodiment the lower bounds of the parent bounding box (parent_lower_x1912, parent lower_y 1914, parent_lower_z 1916) are stored at a level ofprecision determined by the E and K values selected for the CFP datablock 1900. The level of precision of the storage values for the lowerbound of the parent bounding box will generally be set to a higherprecision than the values of the child bounding box (child_lower_x 1924,child_upper_x 1926, child_lower_y 1934, child_upper_y 1936,child_lower_z 1944, child_upper_z 1946), which will be stored as fixedpoint values. A scaled parent bounding box size is stored as a power of2 exponent (e.g., exp_x 1922, exp_y 1932, exp_z 1942). Additionally, areference for each child (e.g., child reference 1952) can be stored. Thesize of the quantized BVH node 1910 can scale based on the width (e.g.,number of children) stored in each node, with amount of storage used tostore the child references and the bounding box values for the childnodes increasing with each additional node.

Logic for an implementation of the quantized BVH node of FIG. 19 isshown in Table 4 below.

TABLE 4 Quantized N-wide BVH Node for Hardware Implementation. structQuantizedNodeHW { struct Float { int1 sign; intE exp; uintK mantissa; };Float parent_lower_x, parent_lower_y, parent_lower_z; intE exp_x; uintMchild_lower_x[N], child_upper_x[N]; intE exp_y; uintM child_lower_y[N],child_upper_y[N]; intE exp_z; uintM child_lower_z[N], child_upper_z[N];Reference child [N]; };

As shown in Table 4, a composite floating point data block (e.g., structFloat) can be defined to represent values for the parent bounding box.The Float structure includes a 1-bit sign (int1 sign), an E-bit signedinteger to store power of 2 exponents (intE exp), and a K-bit unsignedinteger (uintK mantissa) to represent the mantissa used to store thehigh accuracy bounds. For the child bounding box data, M-bit unsignedintegers (uintM child_lower_x/y/z; uintM child_upper_x/y/z) can be usedto store fixed point numbers to encode the relative child bounds.

For the example of E=8, K=16, M=8, and using 32 bits for the childreferences, the QuantizedNodeHW structure of Table 4 has a size of 52bytes for a 4-wide BVH and a size of 92 bytes for a 8-wide BVH, which isa reduction in the structure size relative to the quantized node ofTable 1 and a significant reduction in structure size relative toexisting implementations. It will be noted that for the mantissa value(K=16) one bit of the mantissa may be implied, reducing the storagerequirement to 15 bits.

The layout of the BVH node structure of Table 4 enables reduced hardwareto perform ray-box intersection tests for the child bounding boxes. Thehardware complexity is reduced based on several factors. A lower numberof bits for K can be chosen, as the relative child bounds add additionalM bits of accuracy. The scaled parent bounding box size is stored as apower of 2 (exp_x/y/z fields), which simplify the calculations.Additionally, the calculations are refactored to reduce the size ofmultipliers.

In one embodiment, ray intersection logic of the graphics processorcalculates the hit distances of a ray to axis-aligned planes to performa ray-box testing. The ray intersection logic can use BVH node logicincluding support for the quantized node structure of Table 4. The logiccan calculate the distances to the lower bounds of the parent boundingbox using the higher precision parent lower bounds and the quantizedrelative extents of the child boxes. Exemplary logic for x planecalculations is shown in Table 5 below.

TABLE 5 Ray-Box Intersection Distance Determination floatdist_parent_lower_x = node.parent_lower_x * rcp_ray_dir_x −ray_org_mul_rcp_ray_dir_x; float dist_child_lower_x =dist_parent_lower_x + rcp_ray_dir_x*node.child_lower_x[i]*2{circumflexover ( )}node.exp_x; float dist_child_upper_x = dist_parent_lower_x +rcp_ray_dir_x*node.child_upper_x[i]*2{circumflex over ( )}node.exp_x;

With respect to the logic of Table 5, if a single precision floatingpoint accuracy is assumed to represent the ray, then a 23-bit times a15-bit multiplier can be used, as the parent_lower_x value is storedwith 15 bits of mantissa. The distance to the lower bounds of the parentbounding box on the y and z planes can be calculated in a manneranalogous to the calculation for dist_parent_lower_x.

Using the parent lower bounds, the intersection distances to therelative child bounding boxes can be calculated for each child boundingbox, as exemplified by the calculation for dist_child_lower_x anddist_child_upper_x as in Table 5. The calculation of thedist_child_lower/upper_x/y/z values can be performed using a 23-bittimes 8-bit multiplier.

FIG. 20 illustrates ray-box intersection using quantized values todefine a child bounding box 2010 relative to a parent bounding box 2000,according to an embodiment. Applying the ray-box intersection distancedetermination equations for the x plane shown in Table 5, a distancealong a ray 2002 at which the ray intersects the bound of the parentbounding box 2000 along the x plane can be determined. The positiondist_parent_lower_x 2003 can be determined in which the ray 2002 crossesthe lower bounding plane 2004 of the parent bounding box 2000. Based onthe dist_parent_lower_x 2003, a dist_child_lower_x 2005 can bedetermined where the ray intersects the minimum bounding plane 2006 ofthe child bounding box 2010. Additionally, based on thedist_parent_lower_x 2003, a dist_child_upper_x 2007 can be determinedfor a position in which the ray intersects the maximum bounding plane2008 of the child bounding box 2010. A similar determination can beperformed for each dimension in which the parent bounding box 2000 andthe child bounding box 2010 are defined (e.g., along the y and z axis).The plane intersection distances can then be used to determine whetherthe ray intersects the child bounding box. In one embodiment, thegraphics processing logic can determine intersection distances formultiple dimensions and multiple bounding boxes in a parallel mannerusing SIMD and/or vector logic. Additionally, at least a first portionof the calculations described herein may be performed on a graphicsprocessor while a second portion of the calculations may be performed onone or more application processors coupled to the graphics processor.

FIG. 21 is a flow diagram of BVH decompression and traversal logic 2100,according to an embodiment. In one embodiment the BVH decompression andtraversal logic resides in special purpose hardware logic of a graphicsprocessor, or may be performed by shader logic executed on executionresources of the graphics processor. The BVH decompression and traversallogic 2100 can cause the graphics processor to perform operations tocalculate the distance along a ray to the lower bounding plane of aparent bounding volume, as shown at block 2102. At block 2104, the logiccan calculate the distance to the lower bounding plane of a childbounding volume based in part on the calculated distance to the lowerbounding plane of the parent bounding volume. At block 2106, the logiccan calculate the distance to the upper bounding plane of a childbounding volume based in part on the calculated distance to the lowerbounding plane of the parent bounding volume.

At block 2108, the BVH decompression and traversal logic 2100 candetermine ray intersection for the child bounding volume based in parton the distance to the upper and lower bounding plane of the childbounding volume, although intersection distances for each dimension ofthe bounding box will be used to determine intersection. In oneembodiment the BVH decompression and traversal logic 2100 determines rayintersection for the child bounding volume by determining whether thelargest entry plane intersection distance for the ray is smaller than orequal to the smallest exit plane distance. In other words, the rayintersects the child bounding volume when the ray enters the boundingvolume along all defined planes before exiting the bounding volume alongany of the defined planes. If at 2110 the BVH decompression andtraversal logic 2100 determines that the ray intersects the childbounding volume, the logic can traverse the child node for the boundingvolume to test the child bounding volumes within the child node, asshown at block 2112. At block 2112 a node traversal can be performed inwhich the reference to node associated with the intersected bounding boxcan be accessed. The child bounding volume can become the parentbounding volume and the children of the intersected bounding volume canbe evaluated. If at 2110 the BVH decompression and traversal logic 2100determines that the ray does not intersect the child bounding volume,the branch of the bounding hierarchy associated with the child boundingvolume is skipped, as shown at block 2114, as the ray will not intersectany bounding volumes further down the sub-tree branch associated with achild bounding volume that is not intersected.

Further Compression via Shared Plane Bounding Boxes

For any N-wide BVH using bounding boxes, the bounding volume hierarchycan be constructed such that each of the six sides of a 3D bounding boxis shared by at least one child bounding box. In a 3D shared planebounding box, 6×log₂ N bits can be used to indicate whether a givenplane of a parent bounding box is shared with a child bounding box. WithN=4 for a 3D shared plane bounding box, 12-bits would be used toindicate shared planes, where each of two bits are used to identifywhich of the four children reuse each potentially shared parent plane.Each bit can be used to indicate whether a parent plane is re-used by aspecific child. In the event of a 2-wide BVH, 6 additional bits can beadded to indicate, for each plane of a parent bounding box, whether theplane (e.g., side) of the bounding box is shared by a child. Althoughthe SPBB concepts can apply to an arbitrary number of dimensions, in oneembodiment the benefits of the SPBB are generally the highest for a2-wide (e.g., binary) SPBB.

The use of the shared plane bounding box can further reduce the amountof data stored when using BVH node quantization as described herein. Inthe example of the 3D, 2-wide BVH, the six shard plane bits can refer tominx, max_x, min_y, max_y, min_z, and max_z for the parent bounding box.If minx bit is zero, the first child inherits the shared plane from theparent bounding box. For each child that shares a plane with the parentbounding box, quantized values for that plane need not be stored, whichreduces the storage costs and the decompression costs for the node.Additionally, the higher precision value for the plane can be used forthe child bounding box.

FIG. 22 is an illustration of an exemplary two-dimensional shared planebounding box 2200. The two-dimensional (2D) shared plane bounding box(SPBB) 2200 includes a left child 2202 and a right child 2204. For a 2Dbinary SPBPP, 4 log₂ 2 additional bits can be used to indicate which ofthe four shared planes of the parent bounding box are shared, where abit is a associated with each plane. In one embodiment, a zero can beassociated with the left child 2202 and a one can be associated with theright child, such that the shared plane bits for the SPBB 2200 arennin_x=0; max_x=1; min_y=0; max_y=0, as the left child 2202 shares thelower_x, upper_y, and lower_y planes with the parent SPBB 2200 and theright child 2204 shares the upper_x plane.

FIG. 23 is a flow diagram of shared plane BVH logic 2300, according toan embodiment. The shared plane BVH logic 2300 can be used to reduce thenumber of quantized values stored for the lower and upper extents of oneor more child bounding boxes, reduce the decompression/dequantizationcosts for a BVH node, and enhance the precision of the values used forray-box intersection tests for child bounding boxes of a BVH node. Inone embodiment the shared plane BVH logic 2300 includes to define aparent bounding box over a set of child bounding boxes such that theparent bounding box shares one or more planes with one or more childbounding boxes, as shown at block 2302. The parent bounding box can bedefined, in one embodiment, by selecting a set of existing axis alignedbounding boxes for geometric objects in a scene and defining a parentbounding box based on the minimum and maximum extent of the set ofbounding boxes in each plane. For example, the upper plane value foreach plane of the parent bounding box is defined as the maximum valuefor each plane within the set of child bounding boxes. At block 2304,the shared plane BVH logic 2300 can encode shared child planes for eachplane of the parent bounding box. As shown at block 2306, the sharedplane BVH logic 2300 can inherit a parent plane value for a child planehaving a shared plane during a ray-box intersection test. The sharedplane value for the child can be inherited at the higher precision inwhich the parent plane values are stored in the BVH node structure andgenerating and storing the lower precision quantized value for theshared plane can be bypassed.

FIG. 24 is a block diagram of a computing device 2400 including agraphics processor 2404 having bounding volume hierarchy logic 2424,according to an embodiment. The computing device 2400 can be a computingdevice such as the data processing system 100 as in of FIG. 1. Thecomputing device 2400 may also be or be included within a communicationdevice such as a set-top box (e.g., Internet-based cable televisionset-top boxes, etc.), global positioning system (GPS)-based devices,etc. The computing device 2400 may also be or be included within mobilecomputing devices such as cellular phones, smartphones, personal digitalassistants (PDAs), tablet computers, laptop computers, e-readers, smarttelevisions, television platforms, wearable devices (e.g., glasses,watches, bracelets, smartcards, jewelry, clothing items, etc.), mediaplayers, etc. For example, in one embodiment, the computing device 2400includes a mobile computing device employing an integrated circuit(“IC”), such as system on a chip (“SoC” or “SOC”), integrating varioushardware and/or software components of computing device 2400 on a singlechip.

In one embodiment the bounding volume hierarchy (BVH) logic 2424includes logic to encode a compressed representation of a boundingvolume hierarchy and additional logic to decode and interpret thecompressed representation of the bounding volume hierarchy. The BVHlogic 2424 can work on concert with ray tracing logic 2434 to performhardware accelerated ray-box intersection tests. In one embodiment theBVH logic 2424 is configured to encode multiple child bounding volumesrelative to a reference bounding volume. For example, the BVH logic 2424can encode the reference bounding volume and child bounding volumesusing upper and lower bounds in multiple directions, where the referencebounding volume is encoded using floating point values and the childbounding volume is encoded using fixed point values. The BVH logic 2424can be configured to encode the reference bounding volume as lowerbounds and scaled extents of the bounds and the child bounding volumesusing lower and upper bounds in multiple directions. In one embodimentthe BVH logic 2424 is configured to use the encoded multiple childbounding volumes to encode nodes of a bounding volume hierarchy.

The ray tracing logic 2434 can operate at least in part in connectionwith execution resources 2444 of the graphics processor 2404 includeexecution units and associated logic, such as the logic within agraphics core 580A-N of FIG. 5 and/or the execution logic 600illustrated in FIG. 6. The ray tracing logic 2434 can perform raytraversal through the bounding volume hierarchy and test if a rayintersects the encoded child bounding volumes of a node. The ray tracinglogic 2434 can be configured to calculate bounding plane distances totest for ray bounding volume intersection by calculating distances tothe planes of the lower reference bounding planes and adding to thedistances the arithmetic product of the reciprocal ray direction, thescaled extents of the reference bounds, and the relative child boundingplane location, to calculate the distances to all child bounding planes.

In one embodiment a set of registers 2454 can also be included to storeconfiguration and operational data for components of the graphicsprocessor 2404. The graphics processor 2404 can additionally include amemory device configured as a cache 2414. In one embodiment the cache2414 is a render cache for performing rendering operations. In oneembodiment, the cache 2414 can also include an additional level of thememory hierarchy, such as a last level cache stored in the embeddedmemory module 218 of FIG. 2.

As illustrated, in one embodiment, in addition to a graphics processor2404, the computing device 2400 may further include any number and typeof hardware components and/or software components, such as (but notlimited to) an application processor 2406, memory 2408, and input/output(I/O) sources 2410. The application processor 2406 can interact with ahardware graphics pipeline, as illustrated with reference to FIG. 3, toshare graphics pipeline functionality. Processed data is stored in abuffer in the hardware graphics pipeline, and state information isstored in memory 2408. The resulting image is then transferred to adisplay controller for output via a display device, such as the displaydevice 320 of FIG. 3. The display device may be of various types, suchas Cathode Ray Tube (CRT), Thin Film Transistor (TFT), Liquid CrystalDisplay (LCD), Organic Light Emitting Diode (OLED) array, etc., and maybe configured to display information to a user.

The application processor 2406 can include one or more processors, suchas processor(s) 102 of FIG. 1, and may be the central processing unit(CPU) that is used at least in part to execute an operating system (OS)2402 for the computing device 2400. The OS 2402 can serve as aninterface between hardware and/or physical resources of the computerdevice 2400 and a user. The OS 2402 can include driver logic 2422 forvarious hardware devices in the computing device 2400. The driver logic2422 can include graphics driver logic 2423 such as the user modegraphics driver 1026 and/or kernel mode graphics driver 1029 of FIG. 10.In one embodiment the graphics driver logic 2423 can be used toconfigure the BVH logic 2424 and ray tracing logic 2434 of the graphicsprocessor 2404.

It is contemplated that in some embodiments, the graphics processor 2404may exist as part of the application processor 2406 (such as part of aphysical CPU package) in which case, at least a portion of the memory2408 may be shared by the application processor 2406 and graphicsprocessor 2404, although at least a portion of the memory 2408 may beexclusive to the graphics processor 2404, or the graphics processor 2404may have a separate store of memory. The memory 2408 may comprise apre-allocated region of a buffer (e.g., framebuffer); however, it shouldbe understood by one of ordinary skill in the art that the embodimentsare not so limited, and that any memory accessible to the lower graphicspipeline may be used. The memory 2408 may include various forms ofrandom access memory (RAM) (e.g., SDRAM, SRAM, etc.) comprising anapplication that makes use of the graphics processor 2404 to render adesktop or 3D graphics scene. A memory controller hub, such as memorycontroller hub 116 of FIG. 1, may access data in the memory 2408 andforward it to graphics processor 2404 for graphics pipeline processing.The memory 2408 may be made available to other components within thecomputing device 2400. For example, any data (e.g., input graphics data)received from various I/O sources 2410 of the computing device 2400 canbe temporarily queued into memory 2408 prior to their being operatedupon by one or more processor(s) (e.g., application processor 2406) inthe implementation of a software program or application. Similarly, datathat a software program determines should be sent from the computingdevice 2400 to an outside entity through one of the computing systeminterfaces, or stored into an internal storage element, is oftentemporarily queued in memory 2408 prior to its being transmitted orstored.

The I/O sources can include devices such as touchscreens, touch panels,touch pads, virtual or regular keyboards, virtual or regular mice,ports, connectors, network devices, or the like, and can attach via aninput/output (I/O) control hub (ICH) 130 as referenced in FIG. 1.Additionally, the I/O sources 2010 may include one or more I/O devicesthat are implemented for transferring data to and/or from the computingdevice 2400 (e.g., a networking adapter); or, for a large-scalenon-volatile storage within the computing device 2400 (e.g., hard diskdrive). User input devices, including alphanumeric and other keys, maybe used to communicate information and command selections to graphicsprocessor 2404. Another type of user input device is cursor control,such as a mouse, a trackball, a touchscreen, a touchpad, or cursordirection keys to communicate direction information and commandselections to GPU and to control cursor movement on the display device.Camera and microphone arrays of the computer device 2400 may be employedto observe gestures, record audio and video and to receive and transmitvisual and audio commands.

I/O sources 2410 configured as network interfaces can provide access toa network, such as a LAN, a wide area network (WAN), a metropolitan areanetwork (MAN), a personal area network (PAN), Bluetooth, a cloudnetwork, a cellular or mobile network (e.g., 3rd Generation (3G), 4thGeneration (4G), etc.), an intranet, the Internet, etc. Networkinterface(s) may include, for example, a wireless network interfacehaving one or more antenna(e). Network interface(s) may also include,for example, a wired network interface to communicate with remotedevices via network cable, which may be, for example, an Ethernet cable,a coaxial cable, a fiber optic cable, a serial cable, or a parallelcable.

Network interface(s) may provide access to a LAN, for example, byconforming to IEEE 802.11 standards, and/or the wireless networkinterface may provide access to a personal area network, for example, byconforming to Bluetooth standards. Other wireless network interfacesand/or protocols, including previous and subsequent versions of thestandards, may also be supported. In addition to, or instead of,communication via the wireless LAN standards, network interface(s) mayprovide wireless communication using, for example, Time Division,Multiple Access (TDMA) protocols, Global Systems for MobileCommunications (GSM) protocols, Code Division, Multiple Access (CDMA)protocols, and/or any other type of wireless communications protocols.

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of the computing device 2400 may vary fromimplementation to implementation depending upon numerous factors, suchas price constraints, performance requirements, technologicalimprovements, or other circumstances. Examples include (withoutlimitation) a mobile device, a personal digital assistant, a mobilecomputing device, a smartphone, a cellular telephone, a handset, aone-way pager, a two-way pager, a messaging device, a computer, apersonal computer (PC), a desktop computer, a laptop computer, anotebook computer, a handheld computer, a tablet computer, a server, aserver array or server farm, a web server, a network server, an Internetserver, a work station, a mini-computer, a main frame computer, asupercomputer, a network appliance, a web appliance, a distributedcomputing system, multiprocessor systems, processor-based systems,consumer electronics, programmable consumer electronics, television,digital television, set top box, wireless access point, base station,subscriber station, mobile subscriber center, radio network controller,router, hub, gateway, bridge, switch, machine, or combinations thereof.

Apparatus and Method for Compressing Leaf Nodes of Bounding VolumeHierarchies

The downside of acceleration structures such as bounding volumehierarchies (BVHs) and k-d trees is that they require both time andmemory to be built and stored. One way to reduce this overhead is toemploy some sort of compression and/or quantization of the accelerationdata structure, which works particularly well for BVHs, which naturallylend to conservative, incremental encoding. On the upside, this cansignificantly reduce the size of the acceleration structure oftenhalving the size of BVH nodes. On the downside, compressing the BVHnodes also incurs overhead, which may fall into different categories.First, there is the obvious cost of decompressing each BVH node duringtraversal; second, in particular for hierarchical encoding schemes theneed to track parent information slightly complicates the stackoperations; and third, conservatively quantizing the bounds means thatthe bounding boxes are somewhat less tight than uncompressed ones,triggering a measurable increase in the number of nodes and primitivesthat have to be traversed and intersected, respectively.

Compressing the BVH by local quantization is a known method to reduceits size. An n-wide BVH node contains the axis-aligned bounding boxes(AABBs) of its “n” children in single precision floating point format.Local quantization expresses the “n” children AABBs relative to the AABBof the parent and stores these value in quantized e.g. 8 bit format,thereby reducing the size of BVH node.

Local quantization of the entire BVH introduces multiple overheadfactors as (a) the de-quantized AABBs are coarser than the originalsingle precision floating point AABBs, thereby introducing additionaltraversal and intersection steps for each ray and (b) thede-quantization operation itself is costly which adds and overhead toeach ray traversal step. Because of these disadvantages, compressed BVHsare only used in specific application scenarios and not widely adopted.

One embodiment of the invention employs techniques to compress leafnodes for hair primitives in a bounding-volume hierarchy. In particular,in one embodiment, several groups of oriented primitives are storedtogether with a parent bounding box, eliminating child pointer storagein the leaf node. An oriented bounding box is then stored for eachprimitive using 16-bit coordinates that are quantized with respect to acorner of the parent box. Finally, a quantized normal is stored for eachprimitive group to indicate the orientation. This approach may lead to asignificant reduction in the bandwidth and memory footprint for BVH hairprimitives.

In some embodiments, BVH nodes are compressed (e.g. for an 8-wide BVH)by storing the parent bounding box and encoding N child bounding boxes(e.g., 8 children) relative to that parent bounding box using lessprecision. A disadvantage of applying this idea to each node of a BVH isthat at every node some decompression overhead is introduced whentraversing rays through this structure, which may reduce performance.

To address this issue, one embodiment of the invention uses thecompressed nodes only at the lowest level of the BVH. This provides anadvantage of the higher BVH levels running at optimal performance (i.e.,they are touched as often as boxes are large, but there are very few ofthem), and compression on the lower/lowest levels is also veryeffective, as most data of the BVH is in the lowest level(s).

In addition, in one embodiment, quantization is also applied for BVHnodes that store oriented bounding boxes. As discussed below, theoperations are somewhat more complicated than for axis-aligned boundingboxes. In one implementation, the use of compressed BVH nodes withoriented bounding boxes is combined with using the compressed nodes onlyat the lowest level (or lower levels) of the BVH.

Thus, one embodiment improves upon fully-compressed BVHs by introducinga single, dedicated layer of compressed leaf nodes, while using regular,uncompressed BVH nodes for interior nodes. One motivation behind thisapproach is that almost all of the savings of compression comes from thelowest levels of a BVH (which in particular for 4-wide and 8-wide BVHsmake up for the vast majority of all nodes), while most of the overheadcomes from interior nodes. Consequently, introducing a single layer ofdedicated “compressed leaf nodes” gives almost the same (and in somecases, even better) compression gains as a fully-compressed BVH, whilemaintaining nearly the same traversal performance as an uncompressedone.

In one embodiment, the techniques described herein are integrated withinthe traversal/intersection circuitry within a graphics processor such asthe GPU 2505 illustrated in FIG. 25 which includes dedicated sets ofgraphics processing resources arranged into multi-core groups 2500A-N.While the details of only a single multi-core group 2500A are provided,it will be appreciated that the other multi-core groups 2500B-N may beequipped with the same or similar sets of graphics processing resources.

As illustrated, a multi-core group 2500A may include a set of graphicscores 2530, a set of tensor cores 2540, and a set of ray tracing cores2550. A scheduler/dispatcher 2510 schedules and dispatches the graphicsthreads for execution on the various cores 2530, 2540, 2550. A set ofregister files 2520 store operand values used by the cores 2530, 2540,2550 when executing the graphics threads. These may include, forexample, integer registers for storing integer values, floating pointregisters for storing floating point values, vector registers forstoring packed data elements (integer and/or floating point dataelements) and tile registers for storing tensor/matrix values. In oneembodiment, the tile registers are implemented as combined sets ofvector registers.

One or more Level 1 caches and texture units 2560 store graphics datasuch as texture data, vertex data, pixel data, ray data, bounding volumedata, etc, locally within each multi-core group 2500A. A Level 2 (L2)cache 2580 shared by all or a subset of the multi-core groups 2500A-Nstores graphics data and/or instructions for multiple concurrentgraphics threads. One or more memory controllers 2570 couple the GPU2505 to a memory 2598 which may be a system memory (e.g., DRAM) and/or adedicated graphics memory (e.g., GDDR6 memory).

Input/output (IO) circuitry 2595 couples the GPU 2505 to one or more IOdevices 2595 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 2590 to the GPU 2505 and memory 2598. One ormore IO memory management units (IOMMUs) 2570 of the IO circuitry 2595couple the IO devices 2590 directly to the system memory 2598. In oneembodiment, the IOMMU 2570 manages multiple sets of page tables to mapvirtual addresses to physical addresses in system memory 2598. In thisembodiment, the IO devices 2590, CPU(s) 2599, and GPU(s) 2505 may sharethe same virtual address space.

In one implementation, the IOMMU 2570 supports virtualization. In thiscase, it may use a first set of page tables to map guest/graphicsvirtual addresses to guest/graphics physical addresses and a second setof page tables to map the guest/graphics physical addresses tosystem/host physical addresses (e.g., within system memory 2598).

In one embodiment, the CPUs 2599, GPUs 2505, and IO devices 2590 areintegrated on a single semiconductor chip and/or chip package. Theillustrated memory 2598 may be integrated on the same chip or may becoupled to the memory controllers 2570 via an off-chip interface. In oneimplementation, the memory 2598 comprises GDDR6 memory which shares thesame virtual address space as other physical system-level memories,although the underlying principles of the invention are not limited tothis specific implementation.

In one embodiment, the tensor cores 2540 include a plurality ofexecution units specifically designed to perform matrix operations,which are the fundamental compute operation used to perform deeplearning operations. For example, simultaneous matrix multiplicationoperations may be used for neural network training and inferencing. Thetensor cores 2540 may perform matrix processing using a variety ofoperand precisions including single precision floating-point (e.g., 32bits), half-precision floating point (e.g., 16 bits), integer words (16bits), bytes (8 bits), and half-bytes (4 bits). In one embodiment, aneural network implementation extracts features of each rendered scene,potentially combining details from multiple frames, to construct ahigh-quality final image.

In one embodiment, the ray tracing cores 2550 accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. For example, with respect to the embodiments of theinvention, the ray tracing cores 2550 may include circuitry/logic forcompressing leaf nodes of a BVH. In addition, the ray tracing cores 2550may include ray traversal/intersection circuitry for performing raytraversal using the BVH and identifying intersections between rays andprimitives enclosed within the BVH volumes. The ray tracing cores 2550may also include circuitry for performing depth testing and culling(e.g., using a Z buffer or similar arrangement). Using dedicated raytracing cores 2550 for traversal/intersection operations significantlyreduces the load on the graphics cores 2530. Without these ray tracingcores 2550, the traversal and intersection operations would beimplemented using shaders running on the graphics cores 2530 which wouldconsume the bulk of the graphics processing resources of the GPU 2505,making real-time ray tracing impractical.

FIG. 26 illustrates an exemplary ray tracing engine 2600 which performsthe leaf node compression and decompression operations described herein.In one embodiment, the ray tracing engine 2600 comprises circuitry ofone or more of the ray tracing cores 2550 described above.Alternatively, the ray tracing engine 2600 may be implemented on thecores of the CPU 2599 or on other types of graphics cores (e.g., Gfxcores 2530, tensor cores 2540, etc).

In one embodiment, a ray generator 2602 generates rays which atraversal/intersection unit 2603 traces through a scene comprising aplurality of input primitives 2606. For example, an app such as avirtual reality game may generate streams of commands from which theinput primitives 2606 are generated. The traversal/intersection unit2603 traverses the rays through a BVH 2605 generated by a BVH builder2607 and identifies hit points where the rays intersect one or more ofthe primitives 2606. Although illustrated as a single unit, thetraversal/intersection unit 2603 may comprise a traversal unit coupledto a distinct intersection unit. These units may be implemented incircuitry, software/commands executed by the GPU or CPU, or anycombination thereof.

Node Compression/Decompression

In one embodiment, BVH processing circuitry/logic 2604 includes a BVHbuilder 2607 which generates the BVH 2605 as described herein, based onthe spatial relationships between primitives 2606 in the scene. Inaddition, the BVH processing circuitry/logic 2604 includes BVHcompressor 2609 and a BVH decompressor 2609 for compressing anddecompressing the leaf nodes, respectively, as described herein. Thefollowing description will focus on 8-wide BVHs (BVH8) for the purposeof illustration.

As illustrated in FIG. 27, one embodiment of a single 8-wide BVH node2700A contains 8 bounding boxes 2701-2708 and 8 (64 bit) childpointers/references 2710 pointing to the bounding boxes/leaf data2701-2708. In one embodiment, BVH compressor 2625 performs an encodingin which the 8 child bounding boxes 2701A-2708A are expressed relativeto the parent bounding box 2700A, and quantized to 8-bit uniform values,shown as bounding box leaf data 2701B-2708B. The quantized 8-wide BVH,QBVH8 node 2700B, is encoded by BVH compression 2725 using a start andextent value, stored as two 3-dimensional single precision vectors (2×12bytes). The eight quantized child bounding boxes 2701B-2708B are storedas 2 times 8 bytes for the bounding boxes' lower and upper bounds perdimension (48 bytes total). Note this layout differs from existingimplementations as the extent is stored in full precision, which ingeneral provides tighter bounds but requires more space.

In one embodiment, BVH decompressor 2626 decompresses the QBVH8 node2700B as follows. The decompressed lower bounds in dimension i can becomputed byQBVH8.start_(i)+(byte-to-float)QBVH8.lower_(i)*QBVH8.extend_(i), whichon the CPU 4099 requires five instructions per dimension and box: 2loads (start,extend), byte-to-int load+upconversion, int-to-floatconversion, and one multiply-add. In one embodiment, the decompressionis done for all 8 quantized child bounding boxes 2701B-2708B in parallelusing SIMD instructions, which adds an overhead of around 10instructions to the ray-node intersection test, making it at least morethan twice as expensive than in the standard uncompressed node case. Inone embodiment, these instructions are executed on the cores of the CPU4099. Alternatively, the a comparable set of instructions are executedby the ray tracing cores 4050.

Without pointers, a QBVH8 node requires 72 bytes while an uncompressedBVH8 node requires 192 bytes, which results in reduction factor of2.66×. With 8 (64 bit) pointers the reduction factor reduces to 1.88×,which makes it necessary to address the storage costs for handling leafpointers.

Leaf-Level Compression & Layout

In one embodiment, when compressing only the leaf layer of the BVH8nodes into QBVH8 nodes, all children pointers of the 8 children2701-2708 will only refer to leaf primitive data. In one implementation,this fact is exploited by storing all referenced primitive data directlyafter the QBVH8 node 2700B itself, as illustrated in FIG. 27. Thisallows for reducing the QBVH8's full 64 bit child pointers 2710 to just8-bit offsets 2722. In one embodiment, if the primitive data is a fixedsized, the offsets 2722 are skipped completely as they can be directlycomputed from the index of the intersected bounding box and the pointerto the QBVH8 node 2700B itself.

BVH Builder Modifications

When using a top-down BVH8 builder, compressing just the BVH8 leaf-levelrequires only slight modifications to the build process. In oneembodiment these build modifications are implemented in the BVH builder2607. During the recursive build phase the BVH builder 2607 trackswhether the current number of primitives is below a certain threshold.In one implementation N×M is the threshold where N refers to the widthof the BVH, and M is the number of primitives within a BVH leaf. For aBVH8 node and, for example, four triangles per leaf, the threshold is32. Hence for all sub-trees with less than 32 primitives, the BVHprocessing circuitry/logic 2604 will enter a special code path, where itwill continue the surface area heuristic (SAH)-based splitting processbut creates a single QBVH8 node 2700B. When the QBVH8 node 2700B isfinally created, the BVH compressor 2609 then gathers all referencedprimitive data and copies it right behind the QBVH8 node.

Traversal

The actual BVH8 traversal performed by the ray tracing core 2750 or CPU2799 is only slightly affected by the leaf-level compression.Essentially the leaf-level QBVH8 node 2700B is treated as an extendedleaf type (e.g., it is marked as a leaf). This means the regular BVH8top-down traversal continues until a QBVH node 2700B is reached. At thispoint, a single ray-QBVH node intersection is executed and for all ofits intersected children 2701B-2708B, the respective leaf pointer isreconstructed and regular ray-primitive intersections are executed.Interestingly, ordering of the QBVH's intersected children 2701B-2708Bbased on intersection distance may not provide any measurable benefit asin the majority of cases only a single child is intersected by the rayanyway.

Leaf Data Compression

One embodiment of the leaf-level compression scheme allows even forlossless compression of the actual primitive leaf data by extractingcommon features. For example, triangles within a compressed-leaf BVH(CLBVH) node are very likely to share vertices/vertex indices andproperties like the same objectID. By storing these shared propertiesonly once per CLBVH node and using small local byte-sized indices in theprimitives the memory consumption is reduced further.

In one embodiment, the techniques for leveraging commonspatially-coherent geometric features within a BVH leaf are used forother more complex primitive types as well. Primitives such as hairsegments are likely to share a common direction per-BVH leaf. In oneembodiment, the BVH compressor 2609 implements a compression-schemewhich takes this common direction property into account to efficientlycompress oriented bounding boxes (OBBs) which have been shown to be veryuseful for bounding long diagonal primitive types.

The leaf-level compressed BVHs described herein introduce BVH nodequantization only at the lowest BVH level and therefore allow foradditional memory reduction optimizations while preserving the traversalperformance of an uncompressed BVH. As only BVH nodes at the lowestlevel are quantized, all of its children point to leaf data 2701B-2708Bwhich may be stored contiguously in a block of memory or one or morecache line(s) 2698.

The idea can also be applied to hierarchies that use oriented boundingboxes (OBB) which are typically used to speed up rendering of hairprimitives. In order to illustrate one particular embodiment, the memoryreductions in a typical case of a standard 8-wide BVH over triangleswill be evaluated.

The layout of an 8-wide BVH node 2700 is represented in the followingcore sequence:

struct BVH8Node { float lowerX[8], upperX[8]; // 8 x lower and upperbounds in the X dimension float lowerY[8], upperY[8]; // 8 x lower andupper bounds in the Y dimension float lowerZ[8], upperZ[8]; // 8 x lowerand upper bounds in the Z dimension void *ptr[8]; // 8 x 64bit pointersto the 8 child nodes or leaf data };and requires 276 bytes of memory. The layout of a standard 8-widequantized Node may be defined as:

struct QBVH8Node { Vec3f start, scale; char lowerX[8], upperX[8]; // 8 xbyte quantized lower/upper bounds in the X dimension char lowerY[8],upperY[8]; // 8 x byte quantized lower/upper bounds in the Y dimensionchar lowerZ[8], upperZ[8]; // 8 x byte quantized lower/upper bounds inthe Z dimension void *ptr[8]; // 8 x 64bit pointers to the 8 child nodesor leaf data };and requires 136 bytes.

Because only quantized BVH nodes are used at the leaf level, allchildren pointers will actually point to leaf data 2701A-2708A. In oneembodiment, by storing the quantized node 2700B and all leaf data2701B-2708B its children point to in a single continuous block of memory2698, the 8 child pointers in the quantized BVH node 2700B are removed.Saving the child pointers reduces the quantized node layout to:

struct QBVH8NodeLeaf { Vec3f start, scale; // start position, extendvector of the parent AABB char lowerX[8], upperX[8]; // 8 x bytequantized lower and upper bounds in the X dimension char lowerY[8],upperY[8]; // 8 x byte quantized lower and upper bounds in the Ydimension char lowerZ[8], upperZ[8]; // 8 x byte quantized lower andupper bounds in the Z dimension };which requires just 72 bytes. Due to the continuous layout in thememory/cache 2698, the child pointer of the i-th child can now be simplycomputed by:childPtr(i)=addr(QBVH8NodeLeaf)+sizeof(QBVH8NodeLeaf)+i*sizeof(LeafDataType).

As the nodes at lowest level of the BVH makes up for more than half ofthe entire size of the BVH, the leaf-level only compression describedherein provide a reduction to 0.5+0.5*72/256=0.64× of the original size.

In addition, the overhead of having coarser bounds and the cost ofdecompressing quantized BVH nodes itself only occurs at the BVH leaflevel (in contrast to all levels when the entire BVH is quantized).Thus, the often quite significant traversal and intersection overheaddue to coarser bounds (introduced by quantization) is largely avoided.

Another benefit of the embodiments of the invention is improved hardwareand software prefetching efficiency. This results from the fact that allleaf data is stored in a relatively small continuous block of memory orcache line(s).

Because the geometry at the BVH leaf level is spatially coherent, it isvery likely that all primitives which are referenced by a QBVH8NodeLeafnode share common properties/features such as objectID, one or morevertices, etc. Consequently, one embodiment of the invention furtherreduces storage by removing primitive data duplication. For example, aprimitive and associated data may be stored only once per QBVH8NodeLeafnode, thereby reducing memory consumption for leaf data further.

Quantized Oriented Bounding Boxes (OBB) at the BVH Leaf Level

The effective bounding of hair primitives is described below as oneexample of significant memory reductions realized by exploiting commongeometry properties at the BVH leaf level. To accurately bound a hairprimitive, which is a long but thin structure oriented in space, awell-known approach is to calculate an oriented bounding box to tightlybound the geometry. First a coordinate space is calculated which isaligned to the hair direction. For example, the z-axis may be determinedto point into the hair direction, while the x and y axes areperpendicular to the z-axis. Using this oriented space a standard AABBcan now be used to tightly bound the hair primitive. Intersecting a raywith such an oriented bound requires first transforming the ray into theoriented space and then performing a standard ray/box intersection test.

A problem with this approach is its memory usage. The transformationinto the oriented space requires 9 floating point values, while storingthe bounding box requires an additional 6 floating point values,yielding 60 bytes in total.

In one embodiment of the invention, the BVH compressor 2625 compressesthis oriented space and bounding box for multiple hair primitives thatare spatially close together. These compressed bounds can then be storedinside the compressed leaf level to tightly bound the hair primitivesstored inside the leaf. The following approach is used in one embodimentto compress the oriented bounds. The oriented space can be expressed bythee normalized vectors v_(x), v_(y), and v_(z) that are orthogonal toeach other. Transforming a point p into that space works by projectingit onto these axes:

p _(x) =dot(v _(x) ,p)

p _(y) =dot(v _(y) ,p)

p _(z) =dot(v _(z) ,p)

As the vectors v_(x), v_(y), and v_(z) are normalized, their componentsare in the range [−1,1]. These vectors are thus quantized using 8-bitsigned fixed point numbers rather than using 8-bit signed integers and aconstant scale. This way quantized v_(x)′, v_(y)′, and v_(y)′ aregenerated. This approach reduces the memory required to encode theoriented space from 36 bytes (9 floating point values) to only 9 bytes(9 fixed point numbers with 1 byte each).

In one embodiment, memory consumption of the oriented space is reducedfurther by taking advantage of the fact that all vectors are orthogonalto each other. Thus one only has to store two vectors (e.g., p_(y)′ andp_(z)′) and can calculate p_(x)′=cross(p_(y)′, p_(z)′), further reducingthe required storage to only six bytes.

What remains is quantizing the AABB inside the quantized oriented space.A problem here is that projecting a point p onto a compressed coordinateaxis of that space (e.g., by calculating dot(v_(x)′, p)) yields valuesof a potentially large range (as values p are typically encoded asfloating point numbers). For that reason one would need to use floatingpoint numbers to encode the bounds, reducing potential savings.

To solve this problem, one embodiment of the invention first transformsthe multiple hair primitive into a space, where its coordinates are inthe range [0, 1/√3]. This may be done by determining the world spaceaxis aligned bounding box b of the multiple hair primitives, and using atransformation T that first translates by b.lower to the left, and thenscales by 1/max(b.size.x, b.size.y.b.size.z) in each coordinate:

${T(p)} = {\frac{1}{\sqrt{3}}{\left( {p - {b.{lower}}} \right)/{\max\left( {{b.{size}.x},{b.{size}.y},{b.{size}.z}} \right)}}}$

One embodiment ensures that the geometry after this transformation staysin the range [0, 1/√3] as then a projection of a transformed point ontoa quantized vector p_(x)′, p_(y) ‘, or p_(z)’ stays inside the range[−1,1]. This means the AABB of the curve geometry can be quantized whentransformed using T and then transformed into the quantized orientedspace. In one embodiment, 8-bit signed fixed point arithmetic is used.However, for precision reasons 16-bit signed fixed point numbers may beused (e.g., encoded using 16 bit signed integers and a constant scale).This reduces the memory requirements to encode the axis-aligned boundingbox from 24 bytes (6 floating point values) to only 12 bytes (6 words)plus the offset b.lower (3 floats) and scale (1 float) which are sharedfor multiple hair primitives.

For example, having 8 hair primitives to bound, this embodiment reducesmemory consumption from 8*60 bytes=480 bytes to only 8*(6+12)+3*4+4=160bytes, which is a reduction by 3×. Intersecting a ray with thesequantized oriented bounds works by first transforming the ray using thetransformation T, then projecting the ray using quantized v_(x)′,v_(y)′, and v_(z)′. Finally, the ray is intersected with the quantizedAABB.

The table in FIG. 29 illustrates memory consumption (in MB) and totalrendering performance (in fps) for one embodiment of the invention(CLBVH) implemented on the Intel Embree architecture, including Embree'sregular BVH8 (reference) and Embree's fully-compressed QBVH8 variant; intypical two-in-two Embree BVH configurations: highest performance(SBVH+pre-gathered triangle data) and lowest memory consumption(BVH+triangle indices). Generally speaking, in its two possibleconfigurations (“fast” and “compact”) the embodiments of the inventionhave the same memory savings of Embree's QBVH at much lower performanceimpact (“fast”) or achieves even better compression at roughly the sameperformance impact (“compact”).

The table in FIG. 30 illustrates memory consumption (in MB), traversalstatistics and total performance for two Embree BVH configurations:highest performance (SBVH+pre-gathered triangle data) and lowest memoryconsumption (BVH+triangle indices). One embodiment of the invention(CLBVH) achieves similar or sometimes even greater memory savings as afully compressed BVH, while reducing the runtime overhead to just a fewpercent.

One embodiment utilizes a modified version of the Embree 3.0 [11] CPUray tracing framework. As a comparison framework the publicly availableprotoray path tracer [1] was used. For benchmarking path tracer was setto pure diffuse path tracing (up to 8 bounces) while each CPU HW threadtraces a single ray. For this benchmark 15-20% time is spent in shading.The hardware platform setup is a dual-socket Xeon workstation with 2times 28 cores and 96 GB of memory and as benchmark scenes fourdifferent models with a complexity ranging from 10M to 350M triangleswere tested (using many different camera positions). The performance andmemory consumption was measured for two setups: ‘best performance’ and‘lowest memory consumption’. These two modes required different BVHsettings and primitive layouts: the first pre-gathers all triangles perBVH leaf into a compact layout and uses a BVH with spatial splits(SBVH), while the second mode just stores vertex indices per triangleand uses a regular BVH without spatial splits.

For the best performance, the table in FIG. 30 shows that the overheadof decompressing BVH nodes reduces rendering performance by 10-20%. TheCLBVH approach instead results in only a 2-4% slowdown while providingsimilar or sometimes even slightly larger size reduction (43-45%) of theBVH nodes compared to a full compressed BVH. The size of the primitivedata is unchanged. In terms of total size (BVH+leaf primitive data)these embodiments provide a similar reduction than a fully compressedBVH of 8-10%.

Reducing memory consumption of BVH nodes is more efficient in the memorysetup, where the size of the primitive data is smaller (storing onlyvertex indices instead of full pre-gathered vertices) in relation to thesize of the BVH nodes. The total reduction in memory consumptionincreased to 16-24% when using full compressed BVH nodes or the CLBVHapproach. The CLBVH approach however, does only have 0-3.7% run-timeoverhead while for fully compressed BVH nodes, the overhead rangesbetween 7 and 14%.

For achieving maximum memory reduction a lossless leaf data compressionscheme was employed (see above) to the CLBVH approach. This CLBVH*variant, has a larger run-time overhead than CLBVH but allows forreducing the leaf data (vertex indices per triangle, objectID, etc) sizeby 15-23%, thereby increasing the total size reduction to 26-37%compared to the uncompressed baseline.

REFERENCES

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Apparatus and Method for Motion Blur Using a Dynamic Quantization Grid

As mentioned, motion blur may be used to simulate the effect of objectsmoving in the scene while the camera shutter is open. Simulating thiseffect results in an oriented blur of the moving objects, which causesthe animation to appear smooth when played back. Rendering motion blurrequires randomly sampling the time for each ray path evaluated, and theaverage over many of these paths provides the desired blur effect. Toimplement this technique, the underlying ray tracing engine has to becapable of tracing a ray through the scene at an arbitrary time insidethe camera shutter interval. This requires an encoding of the motion ofthe geometric object inside the spatial acceleration structure used forray tracing.

In practice such a data structure is constructed by building a boundingvolume hierarchy (BVH) over linear motion segments of triangles, wheretriangle vertices are just linearly blended from start to end time.Using many such motion segments allows for encoding a complex motionduring the camera shutter interval, by bounding the motion using linearbounds. These linear bounds store a bounding box for the start and endtime of this motion, such that interpolating these bounds linearly atany time in between yields a proper bounding of the geometry at thatparticular time.

For a ray tracing hardware implementation, it is important thatindividual BVH nodes consume as little memory as possible to reduce nodefetching bandwidth. In one embodiment, local per node quantization ofthe bounding boxes of all children of a wide BVH node is applied. Inparticular, the quantized grid of a wide BVH node encodes the boundingbox of each child using grid coordinates with a small number of bits(e.g. 8-bit vs. 32-bit in full floating point precision).

One embodiment extends this approach to linear bounds for motion blur bystoring quantized bounds for the start and end times using thisquantization scheme for each child. However, with fast motion of verydetailed geometry, this naïve extension is prone to performance issues.The problem is that a small triangle that moves far relative to its sizewill cause the BVH node to store a quite large and thus a coarsequantization grid, which cannot properly bound the small trianglefeature.

One embodiment of the invention solves this issue by not using a staticquantization grid (as in current implementations) but a dynamicquantization grid that moves in accordance with the motion of thebounded child nodes. This embodiment exploits the fact that neighboringgeometry is typically moving in a very similar way; thus, the childrenof a BVH node stay reasonably close together during motion and oftenmove in the same direction.

This property is exploited in one implementation, by determining aquantization grid with fixed extents that moves linearly along thecommon motion of the children of the BVH node. The linear bounds of eachchild can now be mapped into this moving quantization grid, as theresidual motion obtained by subtracting the linear grid motion from thelinear child motion is again a linear motion for which linear quantizedbounds can directly be derived.

The advantage of this technique is that the extents of the movinginterpolation grid only need to be large enough to cover all geometry atthe start time when it is placed at start grid location, and allgeometry at the end time when placed at the end grid location. Thus, thesize depends on the approximate size of the geometry contained insidethe BVH node at start and end time, and not on the volume spanned by theentire animation path. Consequently, the quantization grid will be muchsmaller, reducing storage requirements.

FIG. 31 illustrates an implementation of the naïve extension ofquantized bounding boxes to motion blurred triangles 3101-3103. It isassumed that the BVH node has the three illustrated triangles 3101-3103as children, which move from the left to the right position asillustrated. The quantization grid 3100 for this BVH node over theentire motion is therefore large and can only coarsely bound thetriangles at start and end time.

FIG. 32 illustrates changes employed in one embodiment of the inventionusing much smaller quantization grids which bound the same triangles3201-3203 significantly more tightly. In particular, a start timequantization grid 3200A is translated to an end time quantization grid3200B based on detected motion of the triangles 3201-3203 from left toright. The quantization grid 3200A-B moves linearly along the commonmotion of the children of the BVH node. The linear bounds of each childcan now be mapped into this moving quantization grid 3200A-B, as theresidual motion obtained by subtracting the linear grid motion from thelinear child motion is again a linear motion for which linear quantizedbounds may be directly derived.

FIG. 33 illustrates one embodiment of an architecture for implementingthe motion blur techniques described herein. In operation, a BVHprocessor 3304 constructs a BVH 3300 based on the current set of inputprimitives 3309 of a graphics scene. A ray generator 3301 generates rayswhich traversal circuitry 3305 traverses through the BVH 3307.Intersection circuitry 3310 identifies ray-primitive intersections togenerate hits 3315 which are used for further processing (e.g.,generating secondary rays based on material specifications, etc). One ormore shaders may perform specified shading operations to render theimage frame.

In one embodiment, motion blur processing logic 3312 implements themotion blur techniques described herein based on grid data 3318 anddetected motion of the graphics primitives within BVH nodes. In oneembodiment, a quantization grid motion evaluator 3314 determines themotion of the quantization grid over a specified time period which themotion blur processing logic 3312 uses to perform its motion bluroperations. The motion blur processing logic 3312 may be implemented asprogram code (e.g., an executable shader), circuitry, or using acombination of circuitry and program code. The underlying principles ofthe invention are not limited to any particular implementation of themotion blur processing logic 3312.

One embodiment of a method for motion blur processing is illustrated inFIG. 34. The method may be implemented within the context of thearchitectures described above, but is not limited to any particulararchitecture.

At 3400, a bounding volume hierarchy (BVH) is generated comprisinghierarchically-arranged BVH nodes based on input primitives. At 3401 aquantization grid containing a set of BVH nodes is generated, where eachBVH node includes one or more child nodes. The motion of thequantization grid is determined at 3402 based on detected motion of thechild nodes of a particular BVH node. At 3404, the linear bounds of eachchild node is mapped to the moving quantization grid. In one embodiment,to perform the mapping, one or more residual motion values are obtainedby subtracting the linear quantization grid motion from the linear childnode motion. Linear quantized bounds are then derived from the residualmotion value.

If the child nodes of another BVH node need to be processed, determinedat 3404, then the process returns to 3401 where a new quantization forthe current BVH node is calculated. If not, then the process ends.

Additional details for one embodiment of the invention will now beprovided. It should be noted, however, that the underlying principles ofthe invention are not limited to these specific details.

In one embodiment, the interpolation grid data 3318 includes a startlocation (grid_start), end location (grid_end), and size of the grid(grid_size) that is identical for all time values (i.e., as the grid ismoved based on the movement of the primitives in the scene). In oneembodiment, all of these grid properties are stored as 3D vectors.

The quantization grid motion evaluator 3314 expresses grid motion as:

$\begin{matrix}{{{grid\_ base}({time})} = {{lerp}\left( {{grid\_ start},{grid\_ end},{time}} \right)}} \\{= {{\left( {1.0\text{-}{time}} \right)^{*}{grid\_ start}} + {{time}^{*}{grid\_ end}}}}\end{matrix}$

which is a linear blend for the special case of shutter times 0 and 1.The linear motion of a bounding box, where bounds_start refers to thebounding box at the start time and bounds_end to the bounding box at theend time, can be expressed as:

$\begin{matrix}{{{bounds}({time})} = {{lerp}\left( {{bounds\_ start},{bounds\_ end},{time}} \right)}} \\{= {{\left( {1.0\text{-}{time}} \right)^{*}{bounds\_ start}} + {{time}^{*}{bounds\_ end}}}}\end{matrix}$

This is again a linear motion. In one embodiment, the motionquantization grid motion evaluator 3314 translates the linear bounds ofthe triangle motion bounds (time) into the grid coordinate space toobtain the residual motion residual_bounds (time) relative to the movinggrid:

residual_bounds(time)=(bounds(time)−grid_base(time))/grid_size=(lerp(bounds_start,bounds_end,time)−lerp(grid_start,grid_end,time))/grid_size=lerp(bounds_start−grid_start,bounds_end-grid_end,time)/grid_size=lerp((bounds_start−grid_start)/grid_size,(bounds_end−grid_end)/grid_size,time)=lerp(residual_bounds_start,residual_bounds_end,time)

residual_bounds_start=(bounds_start−grid_start)/grid_size

residual_bounds_end=(bounds_end−grid_end)/grid_size

Thus the grid relative bounds of the triangle at the start time isresidual_bounds_start=(bounds_start−grid_start)/grid_size and the gridrelative bounds at the end time isresidual_bounds_end=(bounds_end−grid_end)/grid_size. The residual motionrelative to the moving grid is just a linear blend of theseresidual_bounds_start and residual_bounds_end positions. Consequently,the linear bounds relative to the moving grid move linearly inside thatgrid itself.

Note that this embodiment obtains a residual linear motion only, as thegrid_size is not linearly blended, but there is just one fixedgrid_size. If grid_size also changes linearly, the quantization gridmotion evaluator 3314 determines the product of two lerp operationswhich does not decompose into the sum of two lerps.

The residual bounds residual_bounds_start and residual_bounds_end caneasily get quantized conservatively using the quantization grid at startand end position to obtain quantized residual boundsquantized_residual_bounds_start and quantized_residual_bounds_end withcorresponding linear interpolation:

quantized_residual_bounds(time)=lerp(quantized_residual_bounds_start,quantized_residual_bounds_end,time)

To obtain the world space dequantized bounds of these, the quantizationgrid motion evaluator 3314 blends the quantized bounds, scaled by thegrid_size factor, and then adds to the blended grid position:

dequantized_bounds(time)=quantized_residual_bounds(time)*grid_size+grid_base(time)

To intersect a ray with linear equation org+t*dir with these bounds thedistances to the bounding planes are determined:

t_lower=(dequantized_bounds(time).lower−org)*rcp(dir)

t_upper=(dequantized_bounds(time).upper−org)*rcp(dir)

This provides the distances to the 3 lower bounding and 3 upper boundingplanes which are then used by the intersection circuitry 3310 to test ifthe bounds are hit using ray/box testing.

In one embodiment, the processing required for the above distancecalculations is reduced using the techniques described above (i.e.,decompression and traversal of a bounding volume hierarchy). Thesetechniques include reducing complexity with a higher precision distancecalculation shared between all children of a node, and adding somecorrections that are determined using the reduced precision quantizedbounds:

$\begin{matrix}{{t\_ lower} = {\left( {{{dequantized\_ bounds}{({time}).{lower}}} - {org}} \right)^{*}{{rcp}({dir})}}} \\{= \left( {{{quantized\_ residual}{\_ bounds}({time})^{*}{grid\_ size}} +} \right.} \\{\left. {{{grid\_ base}({time})} - {org}} \right)^{*}{{rcp}({dir})}} \\{= {{\left( {{{gird\_ base}({time})} - {org}} \right)^{*}{{rcp}({dir})}} +}} \\{{quantized\_ residual}{\_ bounds}({time})^{*}{grid\_ size}^{*}{{rcp}({dir})}}\end{matrix}$

The first term (grid_base(time)−org)*rcp(dir) is determined once for allchildren as it only depends on the quantization grid. The second termquantized_residual_bounds(time)*grid_size*rcp(dir) is determined perchild. However, when the interpolation of the quantized bounds producesa lower precision output, and the grid_size is chosen as a power of 2,then this term is just a product of a small number of bits and afloating point number rcp(dir), which is again cheap to realize inhardware.

In one embodiment, the complexity of calculating the first term isreduced even more as follows:

$\begin{matrix}{{{Term}\; 1} = {\left( {{{grid\_ base}({time})} - {org}} \right)^{*}{{rcp}({dir})}}} \\{= {\left( {{{lerp}\left( {{grid\_ start},{grid\_ end},{time}} \right)} - {org}} \right)^{*}{{rcp}({dir})}}} \\{= {\left( {{grid\_ start} + {{time}^{*}\left( {{grid\_ end}\text{-}{grid\_ start}} \right)} - {org}} \right)^{*}{{rcp}({dir})}}} \\{= {\left( {{grid\_ start} + {{time}^{*}\left( {{grid\_ end}{\_ start}} \right)} - {org}} \right)^{*}{{rcp}({dir})}}}\end{matrix}$

where grid_end_start=grid_end−grid_start is the vector from grid_startto grid_end. When not doing motion blur the formula would look almostthe same, but the time*(grid_end_start) term would be missing. Weattempt to reduce additional complexity to calculate this term byevaluating how much accuracy for grid_end_start is required and how muchaccuracy of time is required. It is sufficient to store 8 mantissa bitsof this grid_end_start term and only use 16 mantissa bits of the time.This significantly reduces hardware complexity for this operation. Thereduction of bits for grid_end_start has to be done in a way that thegrid_end_start vector gets longer (thus the moving grid still containsall geometry). Further reducing the accuracy of the time adds somefuzziness to the grid position, which has to get corrected usingproperly extended residual motion bounds (bounds can simply be extendedby the maximal grid misplacement introduced by this time quantization).

A statistical evaluation of the embodiments described above for sceneswith many small triangles and large motions, reduced the number ofintersection steps per ray by over an order of magnitude compared to thenaïve quantization approach.

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine, module, or logic may beimplemented in firmware, hardware, software, or any combination offirmware, hardware, and software.

EXAMPLES

The following are example implementations of different embodiments ofthe invention.

Example 1. A method comprising: generating a bounding volume hierarchy(BVH) comprising hierarchically-arranged BVH nodes based on inputprimitives, at least one BVH node comprising one or more child nodes;determining motion values for a quantization grid based on motion valuesof the one or more child nodes of the at least one BVH node; and mappinglinear bounds of each of the child nodes to the quantization grid.

Example 2. The method of example 1 wherein mapping linear bounds of eachof the child nodes further comprises: obtaining one or more residualmotion values by subtracting motion values of the quantization grid frommotion values associated with the one or more child nodes; and derivingquantized bounds of the one or more child nodes from the one or moreresidual motion values.

Example 3. The method of example 2 wherein the one or more child nodescomprise a primitive.

Example 4. The method of example 3 wherein the primitive is in motion.

Example 5. The method of example 4 wherein the motion values associatedwith the one or more child nodes are determined based on motion of theprimitive.

Example 6. The method of example 3 wherein the primitive comprises atriangle.

Example 7. The method of example 2 further comprising: performing raytraversal and/or intersection operations in accordance with thequantized bounds of the one or more child nodes to determine one or moreintersection points of a ray.

Example 8. The method of example 7 further comprising: spawning one ormore shaders to perform graphics operations with respect to the one ormore intersection points.

Example 9. A machine-readable medium having program code stored thereonwhich, when executed by a machine, causes the machine to perform theoperations of: generating a bounding volume hierarchy (BVH) comprisinghierarchically-arranged BVH nodes based on input primitives, at leastone BVH node comprising one or more child nodes; determining motionvalues for a quantization grid based on motion values of the one or morechild nodes of the at least one BVH node; and mapping linear bounds ofeach of the child nodes to the quantization grid.

Example 10. The machine-readable medium of example 9 wherein mappinglinear bounds of each of the child nodes further comprises: obtainingone or more residual motion values by subtracting motion values of thequantization grid from motion values associated with the one or morechild nodes; and deriving quantized bounds of the one or more childnodes from the one or more residual motion values.

Example 11. The machine-readable medium of example 10 wherein the one ormore child nodes comprise a primitive.

Example 12. The machine-readable medium of example 11 wherein theprimitive is in motion.

Example 13. The machine-readable medium of example 12 wherein the motionvalues associated with the one or more child nodes are determined basedon motion of the primitive.

Example 14. The machine-readable medium of example 11 wherein theprimitive comprises a triangle.

Example 15. The machine-readable medium of example 10 further comprisingprogram code to cause the machine to perform the operations of:performing ray traversal and/or intersection operations in accordancewith the quantized bounds of the one or more child nodes to determineone or more intersection points of a ray.

Example 16. The machine-readable medium of example 15 further comprisingprogram code to cause the machine to perform the operations of: spawningone or more shaders to perform graphics operations with respect to theone or more intersection points.

Example 17. A graphics processor comprising: a bounding volume hierarchy(BVH) generator to build a BVH comprising hierarchically-arranged BVHnodes based on input primitives, at least one BVH node comprising one ormore child nodes; and motion blur processing hardware logic to determinemotion values for a quantization grid based on motion values of the oneor more child nodes of the at least one BVH node and to map linearbounds of each of the child nodes to the quantization grid.

Example 18. The graphics processor of example 17 wherein to map linearbounds of each of the child nodes, the motion blur processing hardwarelogic is to obtain one or more residual motion values by subtractingmotion values of the quantization grid from motion values associatedwith the one or more child nodes; and derive quantized bounds of the oneor more child nodes from the one or more residual motion values.

Example 19. The graphics processor of example 18 wherein the one or morechild nodes comprise a primitive.

Example 20. The graphics processor of example 19 wherein the primitiveis in motion.

Example 21. The graphics processor of example 20 wherein the motionvalues associated with the one or more child nodes are determined basedon motion of the primitive.

Example 22. The graphics processor of example 19 wherein the primitivecomprises a triangle.

Example 23. The graphics processor of example 18 further comprising: raytraversal and intersection hardware logic to perform ray traversaland/or intersection operations in accordance with the quantized boundsof the one or more child nodes to determine one or more intersectionpoints of a ray.

Example 24. The graphics processor of example 23 further comprising: aplurality of execution circuits to execute one or more shaders toperform graphics operations with respect to the one or more intersectionpoints.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

What is claimed is:
 1. A method comprising: generating a bounding volumehierarchy (BVH) comprising hierarchically-arranged BVH nodes based oninput primitives, at least one BVH node comprising one or more childnodes; determining motion values for a quantization grid based on motionvalues of the one or more child nodes of the at least one BVH node; andmapping linear bounds of each of the child nodes to the quantizationgrid.
 2. The method of claim 1 wherein mapping linear bounds of each ofthe child nodes further comprises: obtaining one or more residual motionvalues by subtracting motion values of the quantization grid from motionvalues associated with the one or more child nodes; and derivingquantized bounds of the one or more child nodes from the one or moreresidual motion values.
 3. The method of claim 2 wherein the one or morechild nodes comprise a primitive.
 4. The method of claim 3 wherein theprimitive is in motion.
 5. The method of claim 4 wherein the motionvalues associated with the one or more child nodes are determined basedon motion of the primitive.
 6. The method of claim 3 wherein theprimitive comprises a triangle.
 7. The method of claim 2 furthercomprising: performing ray traversal and/or intersection operations inaccordance with the quantized bounds of the one or more child nodes todetermine one or more intersection points of a ray.
 8. The method ofclaim 7 further comprising: spawning one or more shaders to performgraphics operations with respect to the one or more intersection points.9. A machine-readable medium having program code stored thereon which,when executed by a machine, causes the machine to perform the operationsof: generating a bounding volume hierarchy (BVH) comprisinghierarchically-arranged BVH nodes based on input primitives, at leastone BVH node comprising one or more child nodes; determining motionvalues for a quantization grid based on motion values of the one or morechild nodes of the at least one BVH node; and mapping linear bounds ofeach of the child nodes to the quantization grid.
 10. Themachine-readable medium of claim 9 wherein mapping linear bounds of eachof the child nodes further comprises: obtaining one or more residualmotion values by subtracting motion values of the quantization grid frommotion values associated with the one or more child nodes; and derivingquantized bounds of the one or more child nodes from the one or moreresidual motion values.
 11. The machine-readable medium of claim 10wherein the one or more child nodes comprise a primitive.
 12. Themachine-readable medium of claim 11 wherein the primitive is in motion.13. The machine-readable medium of claim 12 wherein the motion valuesassociated with the one or more child nodes are determined based onmotion of the primitive.
 14. The machine-readable medium of claim 11wherein the primitive comprises a triangle.
 15. The machine-readablemedium of claim 10 further comprising program code to cause the machineto perform the operations of: performing ray traversal and/orintersection operations in accordance with the quantized bounds of theone or more child nodes to determine one or more intersection points ofa ray.
 16. The machine-readable medium of claim 15 further comprisingprogram code to cause the machine to perform the operations of: spawningone or more shaders to perform graphics operations with respect to theone or more intersection points.
 17. A graphics processor comprising: abounding volume hierarchy (BVH) generator to build a BVH comprisinghierarchically-arranged BVH nodes based on input primitives, at leastone BVH node comprising one or more child nodes; and motion blurprocessing hardware logic to determine motion values for a quantizationgrid based on motion values of the one or more child nodes of the atleast one BVH node and to map linear bounds of each of the child nodesto the quantization grid.
 18. The graphics processor of claim 17 whereinto map linear bounds of each of the child nodes, the motion blurprocessing hardware logic is to obtain one or more residual motionvalues by subtracting motion values of the quantization grid from motionvalues associated with the one or more child nodes; and derive quantizedbounds of the one or more child nodes from the one or more residualmotion values.
 19. The graphics processor of claim 18 wherein the one ormore child nodes comprise a primitive.
 20. The graphics processor ofclaim 19 wherein the primitive is in motion.
 21. The graphics processorof claim 20 wherein the motion values associated with the one or morechild nodes are determined based on motion of the primitive.
 22. Thegraphics processor of claim 19 wherein the primitive comprises atriangle.
 23. The graphics processor of claim 18 further comprising: raytraversal and intersection hardware logic to perform ray traversaland/or intersection operations in accordance with the quantized boundsof the one or more child nodes to determine one or more intersectionpoints of a ray.
 24. The graphics processor of claim 23 furthercomprising: a plurality of execution circuits to execute one or moreshaders to perform graphics operations with respect to the one or moreintersection points.